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Basilique Notre-Dame, Geneva (must see)
The Basilique de Notre-Dame is the main Catholic church in Geneva. It was built according to the design of Alexandre Grigny between 1852 and 1857 on the site of a former stronghold fortifications. This neo-Gothic building, whose appearance is partly inspired by the Beauvais Cathedral, could break ground thanks to the city of Geneva, which had ceded land to religious communities to build places of worship, and through donations and manual labor provided by the Geneva Catholics.
The statue of Our Lady of Geneva, offered by the Pope Pius IX, is venerated; however, it's the basilica's stained glass windows that are particularly remarkable. Some are semi-industrial production neo-gothic, but most show the evolution of the art of stained glass during the twentieth century, in various styles, after those of Claudius Lavergne (installed from 1857 to 1875). From 1912, several artists have successively contributed to adorn the basilica: Charles Brunner, Alexandre Cingria, Maurice Denis, Gherri Moro, Paul Monnier, Jean-Claude Morend, Théodore Strawinsky. The building is classified as Cultural Property of National Significance.
Mon-Sat: 6:30-7:30pm; Sun: 7am-9pm
Image by Gfuerst on Wikimedia under Creative Commons License.
This sight is featured in a self-guided walking tour of Geneva, Switzerland within the mobile app "GPSmyCity: Walks in 1K+ Cities" which can be downloaded from iTunes App Store or Google Play. Please download the app to your mobile phone or tablet for travel directions for visiting this sight. The app turns your mobile device to a personal tour guide and it works offline, so no data plan is needed when traveling abroad.
Basilique Notre-Dame on Map
Create Your Own Self-Guided Walks in Geneva
Sight Name: Basilique Notre-Dame
Sight Location: Geneva, Switzerland
Sight Type: Religious
Guide(s) Containing This Sight:
Famous Religious Sites in Geneva
Geneva Introduction Walk
Walking Tours in Geneva, Switzerland
Create Your Own Walk in Geneva
Creating your own self-guided walk in Geneva is easy and fun. Choose the city attractions that you want to see and a walk route map will be created just for you. You can even set your hotel as the start point of the walk.
Geneva's spiritual life boasts a great number of religious sites that are of historic and cultural value to the city. The city is also the place where the reformist movement started to grow, having theologian Jean Calvin as its leader. Take this tour, whether you are a keen church goer or simply interested in places of spirituality, and visit the most important religious sites of Geneva.
Best Swiss Watch Shops Tour in Geneva
Swiss watches are known as the best, when it comes to quality and precision. Some world-famous brands, such as Patek Phillipe, Swatch, Omega and Piaget were designed and masterminded in Geneva. The watch industry in Geneva dates back to the 16th century, and since then, it has developed rapidly and now makes the most desired watches. Take this tour to choose your watch from the best watch shops in... view more
Geneva Top Architectural Sights Tour
Geneva has always been very much appreciated by tourists, as it provides a variety of attractions. Everyone, who comes to Geneva is fascinated by its medieval look and the marvelous buildings, which are a mix of French and German influences. Take this walking tour if you are interested in learning more about some of the top architectural wonders of Geneva.
Nightlife Walking Tour in Geneva
The night life scene of Geneva offers a variety of venues for a nice evening out, and a unique and spectacular experience. The majority of the bars and pubs are located in the downtown area and are available from the late evening into the early hours of the morning. Take this tour if you are looking for a memorable night out, and a place to relax and enjoy yourself in the city of Geneva
Historic Museum Walk in Geneva
Geneva is a magnificent city with a great number of museums that offer a full spectrum of the city's history. These showcases will enable you to discover the prehistoric Switzerland, and see how the city changed under Roman rule. There are views of medieval Geneva, as well. Take this tour to explore the historical insights of Switzerland and the city of Geneva.
Geneva Art Museums Walk
Geneva is the home of many interesting and impressive art museums. Some of them are unique in the world. A great part of the valuable collections was donated by passionate Genevian collectors, who have managed to obtain the rarest examples of both antique and contemporary art. Large exhibitions of Fine Arts and Oriental Art are most impressive. Take the following tour to discover the most popular... view more
View all walking tours in Geneva, Switzerland


This Paper Apple Weaving Craft is cute, simple to make and perfect for working the fine motors skills of little hands!
The process and motion of weaving in and out of the designated lines is great for hand-eye coordination and the overall effect adds a fun and interesting layer to the apple craft.
Can you imagine how a collection of these apples in both red and green would look on a bulletin board?
And if we're talking about adding additional dimensions and layers, Arty Crafty Kids could add apple prints to the base layer of cardstock.
To see an example of how apple printing works, take a quick look at our Handprint Apple Tree!
Apple Template – this can be found towards the end of the post!
Step 1: Download the template (found towards the end of this post) and print.
Step 2: Cut out the template and trace the apple onto green or red cardstock.
Repeat this process with the green leaf, brown stem and white apple core.
Step 3: Fold the apple in half and cut strips into the apple core, leaving at least 1cm between the stips.
Be careful not to cut too close to the edge to avoid tearing the apple.
Cut a selection of patterned and plain paper into strips (approx 1cm wide).
Step 4: Weave the strips in and out of the cuts in the apple.
Step 5: Glue the edges of the paper strips to keep them from moving out of place.
Step 6: Cut off the excess paper around the apple.
Step 7: Add some apple seeds to the core.
Step 8: Glue the apple core, leaf and stem onto the apple to complete the craft.
And here's an example of this gorgeous Paper Apple Weaving Craft in green!
This is a fab little craft to start the new school year as well as recognising the beginnings of the Apple harvest.
For more easy apple themed crafts, take a look at our Cardboard Tube Apple Trees and Recycled Cardboard Apples that encourages children to create with loose parts.
We also have a super duper round-up of the best Apple Crafts across the internet.


On the internet marketing research is an important tool which a number of the bazaar study firms make use of to compose the data and stats into equipped market research records. Most of the firms do not feel the significance of using online marketplace survey, which we think about one of the most essential tool to get hold of the bazaar share. I can understand the economic crisis period world is facing presently, as well as I make certain on-line exchange study and also online fair study will certainly assist a great deal to consume a pie of bazaar share which might be little but crucial for your organisation. Online market study does not means searching the sites to accumulate the information which could not be authentic. This toll is used by bazaar study firms with access to significant, genuine sources to examine as well as populate the information to be completed in the type of bazaar research records.
Online study is one more vital tool which a number of the business houses as well as firms don't really feel the value of. This is what allow's you to be in touch with previous clients, understand what present customers demands and also what prospective customer anticipates from you. A well drafted set of questions for research analyst will take a form of online survey which firms can make use of to ask the clients or customers to fill out with their thoughts and also responses. The comments produced from online survey will help you recognize what you excellent with, what you lack as well as what they expect more from you.
There are tons of research study firms worldwide, but a qualified study firm is that which recognize well what you require and also what is important for you and your service. The dimension of a study company does not issues but the ability, work experience as well as group they need to use up your tasks. There are occurrences when you call a research study company yet the records provided to you don't fulfill the demands you expect from research study reports. This is where a lot of the research study companies lack. The duty of a research company is to understand the customer requires, of what function the research study records are needed, what is reason behind the online survey the business is looking to have.
Research records and record from on the internet study should resolve the purpose, instead of highlighting worthless statistics which are of no use to you. As a customer you can ask the research company inquiries pertaining to their knowledge. Not just what services they give but likewise what type of group capacities they have, clients they have offered as well as one of the most important they are involved in research study solutions. A study firm always employs research study analysts which have an educational history of business management. The work experience issues but what matters the most is their understanding in particular section.Learn more about Market research Report here.


In our ongoing effort to catalog Netflix, we've got a list of titles that are currently scheduled to leave Netflix throughout May 2018. These include all the movies and TV series that Netflix has either opted to not renew or the owners have decided not to.
Looking ahead, it looks as though some of Disney's TV content is up for renewal. On May 2nd, three titles from Disney's kid's channels are due to expire including the popular series, Phineas and Ferb. As scheduled for removal on May 2nd is Freeform's The Secret Life of the American Teenager. Other titles from Freeform are not currently due to expire.
Just a quick reminder of how this post works. We typically get removal dates around 30 days from when they're due to expire. We update this article throughout the month and at the end, Netflix supplies outlets with an extended list of additional titles also due to expire.
New – for the first time, we're also going to be tracking titles due to expire from Netflix in the United Kingdom for May 2018 too!


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Eleocharis moraosejoana är en halvgräsart som beskrevs av S.González, C.Ulloa och Paul M. Peterson. Eleocharis moraosejoana ingår i släktet småsäv, och familjen halvgräs. Inga underarter finns listade i Catalogue of Life.

Källor

Småsäv
moraosejoana

redpajama_set_name: RedPajamaWikipedia


As determined by a vote by Minor League ballhawks, Fourteen year old Quinn Imiola was voted the 2013 myGameBalls.com Minor League Junior Ballhawk of the Year. Imiola, from Buffalo, New York, snagged 318 balls this season over 51 games including 16 game home run balls.
Attending most of his games at Coca-Cola Field, home of the Buffalo Bisons, Imiola averaged 6.24 balls per game and recorded 13 double-digit games. His high game for the season was 19 balls, a mark he reached twice during the season, once at Coca-Cola Field and once at Eastwood Field. He also currently boasts a streak of 50 consecutive games with at least one ball.
Imiola led all vote-getters with 33 points, including seven first place votes and six second place votes. He edged out last year's winner and fellow Buffalo ballhawk, Danny C, who received six first place votes, six second place votes, and 1 third place vote for a total of 31 points. Danny C had another impressive season, averaging 322 balls and averaging 10.39 balls per game.


Creative professional with extensive insurance and brokering experience encompassing 10 rewarding years. Dedicated Insurance Broker with strong industry ties and relationships. Demonstrates loyalty to clients and provides knowledgeable and compassionate services.
Research insurance company policies and negotiating with underwriters to establish suitability between clients and companies.
Negotiate with insurers on complex specialty polices when necessary, and prepare reports for underwriters and surveyors.
Advise clients how to mitigate risks to commercial and residential properties.
Marketed company services to acquire 43 new business clients in a 3-year time frame.
Fielded clients calls and took meetings to advise clients when to make a claim on their policies.
Amended and renewed existing clients policies and consult potential clients on various policy benefits.
Collected client insurance premiums and processed accounts timely and accurately.
Conducted research and maintained current knowledge of insurance market trends and in clients' industries.
Connected with and maintained business relations with photographers, surveyors, underwriters, and structural engineers.
There are plenty of opportunities to land a Insurance Broker position, but it won't just be handed to you. Crafting a Insurance Broker resume that catches the attention of hiring managers is paramount to getting the job, and LiveCareer is here to help you stand out from the competition.


Suppose I asked, do you want to meet "Michelle," what would you expect? You might wonder, "Does he mean the first lady?" But the more you think about "Michelle," the younger the name seems. If I had to take a guess, I'd say "Michelle" would be closer to a Beatle-like 25. That's the age Michelle Pfeiffer was in Scarface, when her character, Elvira, in an aqua-colored sheath and straight blonde hair, got in the glass elevator to go to dinner with her guy, Frank, and the guy who straightaway reckoned to be her guy, Tony Montana. She was spitting boredom, insolence and contempt. She arrived.
Director Brian De Palma hadn't wanted Pfeiffer in the picture at first, but by the time he came to shoot this scene it was in the star-is-born class. Not that Pfeiffer was Audrey Hepburn or Grace Kelly or Julia Roberts. There was nothing about this woman that said, Look, isn't she lovely! She was in her own world, hardly noticing the men staring at her. Maybe she was stoned, or just shut off in her own glass elevator of sourness and ennui. It seemed as if she didn't want to be in the film, or its boring Miami.
It's hard to think of a big-scene debut that was so unpromising. Elvira was signaling that there was no pleasure to be found in her. She was a pain in the neck or a very pretty woman curled up to protect her own unreachable inner pain. In a lot of movies there would have been the hint that she and Tony Montana were going to save each other. And you can believe that Al Pacino's Tony is half-entertained by that stupid thought. But Michelle Pfeiffer's Elvira was a goner.
Of course, there was a period when Michelle Pfeiffer was a star. Some people said she was the most beautiful woman they'd ever seen. Not that they'd seen her anywhere but on a screen. She was a claw-bright Catwoman in Batman Returns. She was so gorgeous you felt you could smell her summery fragrance in The Witches of Eastwick. And then she was Susie Diamond in a red dress stretched out on a piano singing torch songs in The Fabulous Baker Boys, for which she learned to sing, and in which she is one of the authentic hard cases in American film. Not that I can remember her smiling in that film. Susie Diamond was like broken glass. If you reached out for her you'd likely cut your hand.
These thoughts are provoked because Pfeiffer has a new film, The Family, about a Mafia family that goes into the witness protection program (in rural Normandy!), except that old habits die hard. Pfeiffer is mom, and Robert De Niro is dad. He's Giovanni and she's Maggie. They were the Manzonis and now they're the Blakes. Tommy Lee Jones is the FBI man who is supposed to look after them. Has there ever been a movie with three grumpier stars? Pfeiffer rolls her eyes and shrugs with her mouth, enough to suggest it might be a black comedy, and in the long ago, merry Married to the Mob, she was a sweet kid mixed up with the wrong people. But in The Family, her illusions have gone, and there's a daughter in the film, Dianna Agron, to do the looking cute stuff.
There was a time when Michelle Pfeiffer was getting $10 million a picture and I suppose one of those paydays, What Lies Beneath, counted as a hit. That's the one where her husband, Harrison Ford, is out to get her, but Ford does villainy with about as much heart as Pfeiffer does, "Aren't I great—come and get me." I don't know where the line is drawn that made it clear Michelle didn't want to have to seem great or gettable. She looked as if she'd been crying all night. She seemed gloomy and broody, and even in the alleged love stories, you never felt that she was being moved by what was going on. The last time I felt that being in love meant anything to her was Love Field, The Age of Innocence, and The Russia House (she had a bit of Garbo in that one).
In the years since then, the list of work gets increasingly dank: Wolf (that was Jack Nicholson's film); Dangerous Minds (she was teaching rough kids); Up Close and Personal—that was the one about television, with Robert Redford, and it was terrible; To Gillian on her 37th Birthday; One Fine Day; A Thousand Acres; The Deep End of the Ocean; The Story of Us; What Lies Beneath; I Am Sam; White Oleander; I Could Never Be Your Woman (straight to DVD); Hairspray; Stardust; Cheri; Personal Effects; New Year's Eve; Dark Shadows; People Like Us (as Chris Pine's mother). Some of those aren't as bad as others. But some are far worse. Dark Shadows is lamentable, and Pfeiffer looks wretched about being in it.
What happened? She has been married for decades to David Kelley, the very successful television producer ("LA Law"; "Chicago Hope"; "The Practice"; "Ally McBeal"). They have two children. For all I know it's a happy home where no one takes her films seriously. But they would seem to be prosperous enough that she doesn't need to go on being unhappy. Take Cheri. It's a classic older woman–younger man story, taken from Colette. Christopher Hampton wrote the script and Stephen Frears directed—some 20 years earlier they had worked with Pfeiffer on Dangerous Liaisons, where her character had a lot to cry about. The role of the woman in Cheri runs over with opportunity. It ought to work. But the film was a failure, and it seemed as if something Michelle was reluctant to look old enough, or even to be there.
If you hoped The Family might be funny, think of the last time an actress had a good time with De Niro. Michelle Pfeiffer has had three Oscar nominations in her time, and she made a handful of very entertaining pictures as well as The Fabulous Baker Boys which is in the highest rank. But for years now, she has not seemed to be enjoying her work. There's a simple rule about movies, and it applies to other things—if you want the world to have a good time, start with yourself. As it is, being on screen just seems to rub salt in Michelle's wound: She was pretty once and insolent toward pushy guys. Nowadays, she ought to be called Maud or Magda. Michelle is a dog that don't hunt.


Kumamoto Oysters. Elegant start to get fun intros and conversations going.
Warm Sake. Ah, snowy day drinks are so fab.
Chef TJ's centerpiece display contained the best premium fish flown in from Tokyo just hours before. He was deboning, filleting and slicing in the kitchen during our first segment of girltalk, sake, wine and oysters.
Check out the expert knife cuts.
Sashami Moriawase, Maguro, Shima Aji, Kanpachi, Sake Harami. Okay, now the main event. Every piece was to die for, so fresh and buttery. Intense flavor and silky oily bites. Insane and spectacular!
Ton Juru. Pork soup. The bowl was a bit overcrowded, but everything was outrageously fresh, clean and flavorful.
Shima Aji Oshizushi. This was so fabulous with the sesame seasoned rice.
Mochi Ice-cream. Ah, ice cold heaven! Chewy rice cakes filled with frozen ice-cream. LOVE.
Earlier that day...we had a glorious dump of snow and my 2 biggest boys killed it on the mountain.


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Norway - Luna Kafé - Full Moon 3 - 01/23/97
Midnight Choir
Olsen's Lot
Since we've only spent a few weeks of 1997 yet, I've picked another album from last year for this review. Originally from Skien - the hometown of Henrik Ibsen - the threesome of Midnight Choir made their debut in 1994 with a selftitled album that was well recieved. After gaining experience from playing some hundred gigs in small clubs around Oslo, they went across the Atlantic and recorded their first album outside Austin, Texas. This time, as a quartet, they've travelled even further. Olsens' Lot was recorded in Seattle, Washington, supervised by producer Chris Eckman of Walkabouts-fame.
Olsens' Lot was released the 4th of November, and yet again Midnight Choir shows that most of their inspiration comes from "over there"; from waste deserts, endless highways and deserted bars in the middle of nowhere. Midnight Choir plays rootsy country-rock from the dark side of midnight, filled with melancholy and despair, but yet optimistic in some ways. The strength of Midnight Choir lays in the spacious voice of vocalist Paal Flaata; of many compared with artists like Chris Isaak, which is not all wrong. They present "The Big Music", accompanied by rather delicate string arrangements on most of their songs. All the way they're balancing the thin line between the ambitious and the pompous, and they don't always succeed; sometimes they stumble into the oh-so-typical cliché s of this kind of music.
The best songs are found towards the end of the album. We have The Shadow Of The Circus, with it's gloomy, almost filmatic mood. And the epic The Ballad Of Emma deLoner is quite a beauty, with Carla Torgerson (of The Walkabouts) joining Flaata on vocals. On most of the songs producer Chris Eckman contributes by playing synthesizers and organ, or doing some back-up vocals.
All in all; 12 songs, all written by Al deLoner (alias Atle Bystøm), from up-tempo country-rockers to tearstained ballads. Some ups, and maybe a few more downs. Music of dreaming and drifting, moody and atmospheric. As a comparison I'll mention the songwriting of Lee Clayton. I could drop a few more names, but I won't. That's for the listener to find out. (Well, I will: the slower songs on this album reminded me of Simon Bonney's much overlooked and excellent album Forever (Mute 1992) - other editor's note) To quote some hairy South-Americans: Roots, Bloody Roots! But it ain't that bad! By the way; who's this Olsen-lad?

redpajama_set_name: RedPajamaCommonCrawl


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pharmaceutical manufacturer
The Executive Guide to Contracts and Chargebacks
Published By: CDC Software Published Date: Mar 02, 2011
How Pharmaceutical Companies Smartly Automate to Strengthen Profitability.
cdc software, contracts and chargebacks, pharmaceutical manufacturer, erp system, roi, productivity, tco
Reshaping the Health Care Supply Chain to Achieve Patient Centricity and Competitive Advantage
Published By: UPS Published Date: May 24, 2017
For life sciences businesses, the supply chain used to mean one thing: getting products from factory or warehouse to distributors or ultimate customers as quickly and economically as possible. Now, with today's evolving regulatory environment, an increasingly globalized market, more complex health products, and an increased focus on the patient, pharmaceutical companies and medical device manufacturers are looking at their supply chain and logistics needs in a whole new way.
health care, supply chain, ups, business, science
How a Laboratory Information Management System Can Reduce Errors and Turnaround Time
Published By: STARLIMS Published Date: Apr 16, 2014
Learn how a Laboratory Information Management System reduced turnaround time for all system functions by an average of 50% for a pharmaceutical manufacturer.
starlims, flexible electronic canvas, lims
How a Scientific Data Management System Will Help Cut Costs & Boost Productivity
Learn how a Scientific Data Management System (SDMS) allowed a pharmaceutical manufacturer to drastically cut costs while also boosting the productivity of all of the company's employees.
starlims, sdms, productivity
Independence, in name and fact: Global Pharmaceutical Manufacturer
Published By: KPMG Published Date: Sep 17, 2018
The leadership of a new, marquee pharma manufacturer approached KPMG with a paradox: It was certainly marquee, but not new. As the highly publicized spin-off of a global health services conglomerate, the company began life with $18 billion in annual revenues, an established product pipeline and instant membership in the S&P 100. Reliant on the back-office functions of its parent, it needed to quickly acquire the back-office sinews of a truly stand-alone company: ERP systems and core processes for finance, operations and human resources. During an intensive three-year engagement, KPMG brought the vision of the company's leaders to life, working towards a target operating model through the disciplined implementation of new systems, processes, training and staffing. The multidisciplinary approach eventually touched the lives of every one of the company's 21,000 employees, in 170 countries. And as KPMG's delivered tangible results, something intangible emerged – a new company culture, inde


Seeking investment to purchase, develop and extract gold and other commodities from a lucrative mine in South Africa.
We at Golden Route Investments have identified a property, namely 'Taung', which possesses large gold deposits. There are also large diamond deposits situated within the said property worth hundreds of millions of dollars.
Gross revenue of the mine is projected well above $65 billion. The returns stated exclude the revenue which will be generated from the diamonds and also the by-products such as silver that will be produced once the site is operational. Capital requirements represent less than 1% of the gross worth of the mine. We can provide you with a detailed description of the Taung property and an assessment Report conducted by ABSA.
We are a newly formed company with various strategic partnerships that will give us an edge in the mining industry. We distinguish ourselves by our innovative and cost effective mine strategies.
We are looking for a debt or equity partner with the option to buy out the shares once the debt is repaid. A percentage of 20-40% equity stake is offered.
Get an equity stake in the project after debt is repaid.


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Colin Kaepernick honored at ACLU Bill of Rights Dinner in Beverly Hills
Colin Kaepernick attends the 2017 ACLU So Cal's Bill of Rights Dinner at the Beverly Wilshire Hotel in Beverly Hills. (Video by Brian van der Brug)
By Alene TchekmedyianStaff Writer
Dec. 3, 2017 7 PM PT
Colin Kaepernick, the former San Francisco 49ers quarterback who launched a protest movement last year when he refused to stand for the national anthem before NFL games, was honored Sunday at the ACLU of Southern California's annual Bill of Rights Dinner in Beverly Hills.
Kaepernick was awarded the Eason Monroe Courageous Advocate Award, a recognition that was a surprise to event guests, who gave him a standing ovation. His name was not on the star-studded list of honorees released before the banquet that included Hollywood A-listers Jane Fonda, Viola Davis and Judd Apatow.
For the record:
10:55 p.m. Dec. 3, 2017A previous version of this story gave an incorrect date for Kaepernick's birthday.
"We all have an obligation no matter the risk, and regardless of reward, to stand up for our fellow men and women who are being oppressed with the understanding that human rights cannot be compromised," Kaepernick told the crowd while accepting the award, according to organizers.
Former San Francisco 49ers quarterback Colin Kaepernick was honored Sunday at the ACLU of Southern California's annual Bill of Rights Dinner in Beverly Hills. @AleneTchek @NFL pic.twitter.com/0KV1PPquYS
— Brian van der Brug (@bvdbrug) December 4, 2017
Kaepernick, who turned 30 last month, began protesting before games in response to the large number of black men killed by police. His actions ignited a national debate about political activism by athletes.
"I am not going to stand up to show pride in a flag for a country that oppresses black people and people of color," Kaepernick told NFL Media last year. "To me, this is bigger than football and it would be selfish on my part to look the other way. There are bodies in the street and people getting paid leave and getting away with murder."
Since then, he's been largely quiet about his activism. But several NFL players this season continued what he started, sitting or kneeling during the national anthem. Their actions drew fierce denunciations from President Trump, who called on NFL owners to fire or suspend players who didn't stand for the anthem.
"If NFL fans refuse to go to games until players stop disrespecting our Flag & Country, you will see change take place fast," Trump tweeted in September. "Fire or suspend!"
At least 24 players kneeling this weekend at NFL stadiums that are now having a very hard time filling up. The American public is fed up with the disrespect the NFL is paying to our Country, our Flag and our National Anthem. Weak and out of control!
In March, Kaepernick became a free agent, opting out of his contract with the 49ers. Since then, he has been unable to find a team willing to hire him. Last month, he filed a grievance against the NFL, alleging that team owners have colluded against him.
Hector Villagra, executive director of the ACLU of Southern California, told the crowd that Kaepernick "took a stand knowing he would risk his job," organizers said.
"He has lost his job, one that he loved and was supremely talented and skilled at," Villagra said. "He took a stand knowing that some would criticize him, and he has been viciously and unfairly criticized. He has been called a traitor because too many people in this country confuse dissent for disloyalty."
alene.tchekmedyian@latimes.com
7:20 p.m.: This article was updated with comments from the event.
This article was originally published at 6:55 p.m.
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Alene Tchekmedyian
Alene Tchekmedyian is an investigative reporter at the Los Angeles Times. She previously covered the Los Angeles County Sheriff's Department, focusing on accountability stories and writing about failures by officials to comply with transparency laws. Before joining The Times in 2016, she reported on crime and policing for the Glendale News-Press and Burbank Leader.
10:55 p.m. Dec. 3, 2017: A previous version of this story gave an incorrect date for Kaepernick's birthday.
How Las Vegas declared war on thirsty grass and set an example for the desert Southwest
8 stunning SoCal sea caves to visit during the winter low tides
Latest California
Winter storm brings snow, rain to Southern California; CHP escorting traffic over Grapevine
From death row, a phone gave them a love connection. It led to murder
Contraband cellphones, coded messages help Mexican Mafia operate in California prisons


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Home » Automotive » Top 10 Best Car Vacuum Cleaners in 2022 Reviews
Top 10 Best Car Vacuum Cleaners in 2023 Reviews
Automobiles are an essential part of our lives. Many people take great care to maintain the exterior of their automobiles. The only bit of problem is that keeping the interiors clean is not that easy. You have to eliminate dirt, and contaminants from the interiors of your car regularly. Doing so is not an easy task. The best solution is to opt for car vacuum cleaners.
The car vacuum cleaners come in all shapes and sizes. The features of car vacuum cleaners are also different. That is why; it can be confusing to pick the right one. We will list some of the parameters which you should consider while choosing such a car vacuum cleaner.
Things to Consider While Buying Car Vacuum Cleaners
The parameters which you should consider are:
Battery power: Most car vacuum cleaners can be used without an electrical connection. They consist of rechargeable batteries. You have to look at the capacity of the battery and the time for which it can work on a single charge. Once you consider this factor, it is easy to choose the vacuum cleaner.
Tank capacity: You have to check the size of the tank and the vacuum cleaner. The larger the tank size, the more debris it can collect in a single instance. If you're looking for a smaller one, you can go with a tank capacity of 2 gallons to 6 gallons. If you're looking for the larger one, you can go with a tank capacity of 8 gallons to 14 gallons.
Size: You have to check the size of the car vacuum cleaners. The more compact it is, the easier it is for you to store it in your car. You have to look at the size and compare the different options and then take a call.
Accessories: The accessories of any car vacuum cleaner are essential. You should ideally choose the one which comes with a few accessories. The right nozzle can help you in removing the dust and contaminants from the corners and below the seats. That is why; you have to take into account the accessories which come along with it.
Once you compare the car vacuum cleaners based on these factors, it is easier for you to choose the best car vacuum cleaner. We will now highlight the top 10 options from which you can choose.
List of Best Car Vacuum Cleaners in 2023
#10. Bissell Garage Pro Wet Dry Car Vacuum
#9. BISSELL Cordless Car Hand Vacuum
#8. BLACK+DECKER Dustbuster Cordless Vacuum
#7. BLACK+DECKER BDH2000PL Vacuum
#6. HOTOR Car Vacuum
#5. ThisWorx for TWC-01 Car Vacuum
#4. MotorFansClub Handheld Vacuum
#3. Corux Car Vacuum Cleaner
#2. EletecPro Handheld Car Vacuum Cleaner
#1. ZIGLINT Handheld Cordless Vacuum Cleaner
ThisWorx Car Vacuum Cleaner - Car Accessories - Small 12V High Power Handheld Portable Car Vacuum... $27.99 View Products
FRESMOL Car Vacuum, Portable Car Vacuum Cleaner High Power 8000PA/100W/DC12V, 16.4Ft Corded Handheld... $29.99 View Products
Handheld Vacuum Cordless, 10000PA Powerful Suction Car Hand Held Vacuum Cleaner with Large-Capacity... $69.99 View Products
Hand Held Vacuuming Cordless Rechargeable-10K PA Strong Suction Car Vacuum Cordless... $39.99 View Products
Armor All, AA255 , 2.5 Gallon 2 Peak HP Wet/Dry Utility Shop Vacuum , Orange $61.11 View Products
IMINSO Handheld Vacuum Cordless Car Vacuum with 9000PA, Lightweight Rechargeable Hand Vacuum... $45.99 View Products
Car Vacuum Handheld Vacuum 9000PA Suction Car Vacuum Cleaner with LED Light,Car Vacuum Cordless... $27.79 View Products
Chuboor Handheld Vacuum 16000pa, Strong Powerful Mini Car Cleaner, Dust Busters Cordless... $99.99 View Products
ThisWorx Car Vacuum Cleaner - LED Light, Portable, High Power Handheld Vacuums w/ 3 Attachments, 16... $32.99 View Products
VacLife Handheld Vacuum, Car Vacuum Cleaner Cordless, Red (VL188-N) $44.99 View Products
The car vacuum cleaner which we are speaking about now can remove wet and dry debris without any problem. It comes along with the wall mounting kit and a long hose which allows you to remove the contaminants without any problem. It is suitable for cleaning debris from the upholstery, carpet as well as the floor of the car.
It consists of 7 different attachments which mean that you can use it without any problem. The 4-gallon tank allows you to eliminate a large amount of debris. The 2 stage filtration system means that you can contain the dust and other contaminants inside. All of these features make it a pretty good option when looking for a car vacuum cleaner.
Comes along with seven attachments
Comes along with wall mounting unit
4-gallon tank capacity
Construction quality is not satisfactory
Product Dimensions: 5.4 x 10 x 14.6 inches
If you're looking for a vacuum cleaner with powerful suction, you can consider this option. It comes along with the 22 V lithium-ion battery which is rechargeable. With the help of turbo brush, you will be able to remove the dust and contaminants from under the seats without any problem at all.
It comes along with the 2-foot long extension hose which means that you can remove the dust and contaminants easily from any area of the car. Moreover, with the help of LED lights, you can be sure that cleaning in the dark is easy. It especially comes in handy when cleaning your car. Thus, if you're looking for a vacuum cleaner tailor fit for car cleaning, you can consider this option.
Long extension hose
The built-in hose is not up to the mark
The cordless vacuum cleaner which we are discussing now has a lithium-ion battery. It has a long life and adequate suction power at 15.2 AW. It is energy efficient and uses 50% less energy as compared to its peers.
With the help of cyclonic action, it is easier for you to remove the dirt and contaminants without any problem. It comes along with the slim nozzle which means that you can remove the pollutants from corners and under the seats without any problem. With all these features combined, you will realize why it is such a good option.
Adequate suction power
Energy-efficient
The battery is not replaceable
Product Dimensions: 18 x 6.9 x 6.8 inches
With strong suction power and lithium battery, you cannot go wrong with this car vacuum cleaner. It is suitable for cleaning every area in the car. The suction power is 35 AW, and the weight is just 3 lbs. As a result, you can use it without any problem.
Moreover, the high-performance motor provides good suction power. The cyclonic action means that the stubborn dirt and contaminants can get eliminated. It takes just 4 hours to charge it, which is an added advantage. The 3 stage filtration system means that the debris collected remains inside the tank. Hence; it is such an efficient car vacuum cleaner.
Strong suction power
4 hours of charging time
3 stage filtration system
Packaging is not up to the mark
The next option on our list is available in 2 different colors. It is perfect for cleaning a car with its long-lasting battery and adequate suction power. It is lightweight which means that you can move it around quickly. The stainless steel filter allows you to gather dust and contaminants and keep them inside.
With the help of LED lamp, you can get proper illumination. It comes along with accessories like an extended tube, nozzle, and brush. Thus, you don't need to worry about investing in accessories separately. The washable filter ensures that you can keep it clean without any problem. All of these features make it a pretty good choice when looking for a car vacuum.
Comes along with accessories
Suction power can be improved
See also Top 10 Best Car Electric Kettles in 2023 | Buyer's Guide
Product Dimensions: 13.4 x 4 x 4.3 inches
The next car vacuum cleaner which we are discussing comes along with the entire package. It includes a carry bag and various accessories. It is available in 2 different colors which ensure that you can choose the one according to your requirement. You can get professional results with the help of this vacuum cleaner. It has strong suction power due to the 106 W motor which is present in it. With the help of metal turbine, you can be sure that all the debris is collected and remains inside the vacuum cleaner.
Moreover, it consists of 16 feet long power cord which means that connecting it is not a problem at all. The cable allows you to connect it to your car specifically the cigarette lighter. The ergonomic design allows you to use it for a long time quite easily. It comes along with nozzle, carry bag, brush and various other accessories. All in all, if you're looking for a vacuum cleaner which is a complete package, you can consider this option.
Comes along with carrying bag
Cannot be connected to the regular electrical outlet
The next option on our list is a multipurpose car vacuum cleaner. You can use it in your home as well. It comes along with a wide variety of accessories. It is perfect for cleaning liquids as well as solids. It is lightweight at only 1.7 lbs. The lightweight ensures that you can move it around without any problem. It consists of a 12 V DC motor which has a rating of 120 W. The suction power of 5000 PA means that you can use it to clean the debris without any problem. The handheld vacuum cleaner has high suction power and a battery of 2500 mAh. On a single charge, it can run for 30 minutes.
It comes along with a wide variety of accessories like nozzle, extended the handle, the dust brush, liquid nozzle, storage back in-case, you have any doubts regarding the operation of the vacuum cleaner, you can use the manual which comes along with it. Thus, whenever you're looking for a multipurpose vacuum cleaner which you can use in your car as well, it is a good idea to consider this option.
Comes along with various accessories
High suction power
Comes along with a storage bag
Product Dimensions: 14.8 x 6.4 x 6 inches
With 150 W motor, you can be sure that the central vacuum cleaner is perfect for cleaning the debris and contaminants from your car. It has a suction power of 3500 PA. With the help of the cyclonic system, it is easier for you to eliminate the dust and contaminants. It is perfect for cars. It can handle the stubborn dirt and contaminants and also reaches the difficult to access spots in the car.
With the help of the stainless steel dust filter, you can be sure that it is easy to collect the dust and also keep it inside the tank. With the weight of justice 1.7 lbs., you can move it around without any problem. It comes along with a 13 feet cord which means that you can use it throughout the car without any issues. Thus, when looking for a compact but powerful car vacuum cleaner, you can consider this option.
Does not come along with attachments
If you're looking for a car vacuum cleaner with strong suction power and proper illumination, you should consider this option. It has a 90 W motor. The suction power is 4000 PA. It is designed only for car usage. With 360 whirlwind technology, you can be sure that it can eliminate the dust and contaminants from the corners as well.
It is easy to use and also the filter and canister are easy to wash. It comes along with three different nozzles which are perfect for every type of application. The light illumination ensures that you can use it in darkness without any problem. It comes along with a carry bag which means that you can store it in your car. The nozzle, as well as the high suction power, makes it a pretty good choice.
360 whirlwind technology
Comes along with three nozzles
Incorporates light source
Comes along with a carry bag
Brush quality is not that good
The option on top of our list has a sleek design. It has a suction power of 6 kPA. The weight is just 1.87 lbs which means that you can move it around without any problem. It supports wet as well as dry suction. As a result, you can use it for cleaning any debris. With the help of aluminum alloy fan, you can be sure that the stubborn debris can also get eliminated. It is cordless in operation which means that you can move it around without any issue. It has a runtime of 30 minutes with the battery of 2000 mAh.
When you look at the accessories, it comes along with nozzle, extended nozzle, dust brush, head tools. The light illumination means that you can use it in complete darkness without any problem. The battery is removable and rechargeable. It comes along with the washable bowl and filters which allow you to keep it clean without any problem. Thus, every feature which you might look for in a car vacuum cleaner is present in this one.
Suitable for wet and dry suction
Run time of 30minutes on a single charge
Washable bowl
Removable battery
Incorporates a light source
Charges only in the vertical stand
If you're looking for the best car vacuum cleaners available out there, these are the top 10 options available. When you choose any vacuum cleaner from these ten options, it will be easier for you to keep your car clean. These vacuum cleaners are not only efficient but ensure that you can cover every space inside your automobile and keep it immaculate. It is time to pick the right car vacuum cleaner to keep your car completely clean.
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- Also on today: Kuurne-Brussels-Kuurne LIVE.
- Also on today: Omloop van het Hageland LIVE.
- Next update as live video coverage gets underway.
- Coverage should get underway at around *2:00pm CET (*8:00am U.S. Eastern). *Website gives this time, French TV guide says 2:30pm, other sources say 2:15pm.
- Start list for Sunday's Drome Classic here.
- Coverage should get underway at around 4:00pm CET (10:00am U.S. Eastern).
- The Start List for Saturday's Classic Sud-Ardeche is here.


and the distance to other magnet and charter options.
We have partnered with Durham Public Schools (DPS) to have them directly link to durhamschoolnavigator.org vs. the current mapping tool.
Durham Charter Collaborative, Durham PTA Council, and DPS are supporting Code for Durham in launching the School Navigator and the principal survey for the school profiles.
The code base for School Navigator is located at https://github.com/codefordurham/school-navigator. The README file shows links to documentation.
Vallyre and Lauren went over front-end Github issues to tackle ahead of August Spark Day. Looking forward to sharing this project at the event!
At the 12/13 meeting, Basia worked on converting profile change recommendations into Github issues. Jeremy, Alex, Nicole and Lauren discussed immediate next steps for the project. Lauren, Basia and newcomer Vallyre went over the profile design change recommendations.
Bendte, Basia, and Kesley, have gone through the screen capture and various people's notes from usability testing sessions on the school profile page, and we have combined them into a recommendations document.
Kesley will start turning those bullet points into GitHub issues. Bendte and Basia will help as needed, and anyone else is welcome to jump in. So hopefully we can start getting some of this stuff done.
Merged pull requests from last two meetings and gave feedback.
Great work today by all - thanks to everyone for joining!
— Victor worked on fixing the photo upload features - in the coming days he plans to update the survey such that schools can upload the photo themselves. — Nicole will send an update to the reminder email to include the note about photos - the reminder is planned to go out next meeting (7/26) to give appropriate time for people who haven't started to get the survey in. (Currently due 7/29) — Basia, Bendte, and Lauren were doing a "card sorting" design exercise to help enhance the user experience on the website. — Lauren Hirsch and John Fredrickson got added to the github. — Alex and John were working on integrating the charter listing page to link to/be consistent with the other school listings. — Seth followed up to get a codeforamerica domain for an email address for the project team. — Jeremy worked on a link from the mini-profile to main profile on the search results. — David continued to assess NCDPI data.
We launched the School Profile Survey to DPS tonight!!!
Tonight was Sarah's last night at Code for Durham, Thank You for all you have done for the School Navigator project. We wish you the best and will miss you.
@RolfBates added a migration to add a standardized state_id to Schools. This can be used to add state data to schools.
@schpatel (Seth) started to set up email account for durham school web domain. Will flesh out outreach plan for real estate agents.
@npage915 (Nicole) Working with Jeremy on figuring out special case charter schools. Multiples location or multiple school leaders. Added new schools.
@jtf621 (Jeremy) worked on adding relationships to the School model to represent schools "feeding" other schools.
In the last week @basiacoulter and @bendte worked on a plan for a round of UX improvements. They worked on a plan for reaching out to the community and A/B testing multiple options.
Outstanding items for launch: More QA & Testing!
There's a sample schools: https://durhamschoolnavigator.org/#/school/247/ You can set your email to the principal email. Send yourself a survey. Fill it out. We will be using that sample school as a demo in our meeting next Tuesday at 9am….
We will be at the meeting of school principals next week to demo the project and ask for everyone to fill out the survey.
Alex and Sarah met with the Durham Charter Collaborative (which is board members and principals from Durham's charter schools) last week and they were very excited about the site. They all agreed to complete the survey once it's ready.
Sarah is reaching out to a handful of administrators from the 3 types of schools to get their feedback on the profile and confirm that we're not a) using the wrong language/categorizations anywhere b) asking for unrealistic info c) get their buy-in. So far so good.
Onboarded Melissa - who made her first commit - Awesome!
Sarah —- Shared & reviewed survey results. Starting to write up summary.
Alex —- Introed Iron Yard student ( https://github.com/jeremyrist ) to project, merged his PR, and deployed.
Adam & Colin —- Met with PTA council. Discussed further outreach opportunities.
mega props for megamilestones, team!
Victor removed school list page. Added hours and renamed description -> mission statement. Frontend now shows mission statements.
Fixes: map doesn't load most of the time.
New: each address has a unique url that can be shared.
New: Hovering over a school on the left panel highlights it on the map.
Removed selected school district when a tab is clicked.
Uncovered the reason why the option tab is not showing the list of school: the data is malformatted.


My daughter has a Master's degree and she is employed in Private Sector as a Human Resources Professional currently based in Noida. We belong to a Middle class, Nuclear family with Moderate values.
I am a software professional with a Master's degree currently working in private sector as a software engineer in pune.
My daughter is senior researcher and pursing Ph.D degree from Lucknow University. We belong to an upper middle class, nuclear family with traditional values. People describe her as humble, knowledgeable, well-mannered, good-natured and sweet. Her hobbies and interests are cooking, photography, listening to music, nature, dancing and internet surfing.
My sister is a doctor with a Master's degree working in government sector. She is currently living in Agra. We come from an upper middle class, nuclear family background with moderate values.
My name is Sarika Yadav and I have completed my B.Com.. I belong to a Upper Middle Class, Joint Family with Moderate Values.
My name is Jyoti Yadav. I have completed my CA. I grew up in a Middle Class, Nuclear family with Moderate.
She lives in Agra. She comes from a Middle class, Nuclear family background with Traditional values.
We belong from educated family & believe in family values and culture .
My name is Dr. Anurag and I have completed my MD / MS (Medical). I belong to a Upper middle class, Nuclear family with Moderate Values.I have just completed my MS Orthopaedics.
My name is Sanjeev Yadav and I am work as a Professor / Lecturer in the Private Sector having designation of HOD in physics Dept. I have completed my B.Tech. in mechanical engineering from VIT, Vellore. I have been raised up in a Upper Middle Class, Nuclear Family with Moderate Values.
My son is self-employed with a master's degree currently doing SR ship from Kgmu Lucknow . We come from an upper middle class, nuclear family background with moderate values. People describe him as honest, hard-working, smart, down-to-earth and handsome.
I m a simple boy, wants to live my life in my own way, I dont want that somebody interfere in my life..
I am working as a Teaching / Academician in the Private sector. I have completed my M.Ed. M.A (eco) MBA and also UGC-NET. I grew up in a Upper Middle Class class, Nuclear family with Liberal values. � I would describe myself as someone who is honest, caring, intelligent, hardworking, and ambitious. I have a great sense of humour. I am an easy going person & dont get easily disturbed by downs in my life. I a spiritual person & have a good library of best spiritual books. I also enjoy travelling, watching movies, going out for dinner, and having great intellectual conversations!
I am employed in Government Sector as a Banking Service Professional with a Bachelor's degree. I currently live in Agra. I come from an Upper middle class, Nuclear family background with Moderate values.
We are looking for tall beautiful fair girl with high moral values for our son.He has started his career after completing B.Tech & MBA. He is working as Asst. Manager in one of the reputed company (Gurgaon). Aman has one elder sister who is squadron leader in Indian Air force and she is married to pilot in Hissar haryana and leading happy married life.Her husband is also serving Indian Air-force . Myself Shashi Yadav mother of Aman Yadav working as a teacher in CBSE school and my husband is Inspector in U.P. Police. My son is state level badminton player .Ours is upper middle class family .We belong to Etawah but settled in Agra.We have our own house and other properties in both the places.
I am a Manager with a Master's degree working in Government sector. I am currently living in Agra. I come from an Upper middle class, Joint family background with Traditional values.
I have a Bachelor's degree and I am employed in Private Sector as a Technician currently based in Agra. I come from an Upper middle class, Joint family background with Traditional values.
Hi, I have done MBA finance from pune & right now working as Financial Analyst in Public company (Genpact) based in Pune. I grew up in a Upper middle class, Nuclear family with Moderate values.


Grab PH Denies Micab's 29,000 Phantom Booking Allegations - Claims They Have "Malicious Intent"
Last week, Micab's founder Eddie Ybañez shared an article on e27, revealing that its ride-hailing platform has been hit with 15,000 phantom bookings.
This number has nearly doubled in a span of two months to over 29,000 this month.
In the article, Ybañez shared further that Micab's internal investigation points to their rival Grab.
"From our 29,000 phantom bookings, we've received over several hundred reports of Grab soliciting our taxi drivers to attend their driver orientations. This is our single most compelling piece of evidence pointing to them as the culprit of the phantom bookings," Ybañez told BusinessWorld.
He added that their investigations revealed that the SIM numbers used to book the rides are sequential, which means that they were probably bought in batches and the bookings come from a single entity.
In response to these allegations, Brian Cu, country head of Grab Philippines said that they carried out internal investigations last week and they can now "confirm that Eddie Ybañez's allegations are untrue and appears to have malicious intent".
He also urged Ybañez to be "responsible in his speech and actions"; and if he continues to make further false allegations, Grab will not hesitate to take libel action.
Grab however, is unable to share details of their investigations due to legal issues, but promises to be transparent with their findings when the time is right.


text:
Scaling Online ML Predictions At DoorDash
DoorDash is a 3-sided marketplace that consists of Merchants, Consumers, and Dashers.
As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and the ranking of the large volume of search queries.
The prediction service built to meet above use cases now supports many dozens of models spanning different Machine Learning algorithms such as gradient boosting, neural networks and rule-based. The service supports greater than 10 billion predictions every day with a peak hit rate of above 1 million per second.
In this session, we will share our journey of building and scaling our Machine Learning platform and particularly the prediction service, the various optimizations experimented, lessons learned, technical decisions and tradeoffs made. We will also share how we measure success and how we set goals for the future. Finally, we will end by highlighting the challenges ahead of us in extending our Machine Learning platform to support the Data Scientist community and a wider set use cases at DoorDash.
In this session watch:
Hien Luu, Sr. Engineering Manager, DoorDash
Hien Luu: Hi, everyone. Welcome to the Scaling ML Predictions at DoorDash session. My name is Hien Luu and I lead the ML platform team. In this presentation, I will present the first part, and Arbaz, an engineer on my team, will present the second part.
First, I will share a little bit about the DoorDash marketplace, then briefly talk about a few interesting machine learning used cases, and discuss some details about the approach and the strategies we use for building our ML platform. Arbaz will talk about a main part of the presentation, which is the Scaling ML Online Predictions. Then, we'll wrap up with a few lessons learned and future work.
DoorDash mission is to grow and empower local economies, and we do that through a set to product offerings. First, delivery and pick up, where customers like you and me can order food on demand for breakfast, lunch or dinner or for various parties and similar locations. Second, an emergent product convenience and grocery, where customers can order non-food related items such as flowers, charcoals for the barbecue, or even sugar for baking cookies. The product is called DoorDash pass, a subscription product that is similar to the Amazon Prime. DoorDash also provides a logistics platform that powers the delivery for some of the notable merchants that you see in the slide, like Chipotle, Walmart or Target.
Now, DoorDash platform is a three-sided marketplace, and like most marketplaces, it has the flywheel effect, and we'll share some details about that. Starting at the center and moving to the right, as we bring on more customers there will be more orders, and that creates more earnings for Dashers, and that improves the delivery speed and efficiency for the marketplace. Now, starting at the center and moving to the left. Again, as we have more customers, then there will be more revenues for the merchants. The more merchants we have on the marketplace, the more selections there are for customers like you and me. Now, as this flywheel speeds up, it brings benefits to all three sides. It drives more growth for margins. It creates greater earnings for Dashers and brings convenience and selection for all of us as customers.
Now, let's talk about the machine learning use cases at DoorDash. I would describe these use cases by going through the food ordering steps, which consists of creating order, order checkout, dispatching order and delivering order. Each of these steps has its own interesting machine learning used cases. Now, let's start with creating order. This is where customers like you and me land on the homepage and decide what to order for breakfast, lunch, or dinner. As far as recommendation goes, we would like to provide a personalized food ordering experience for customers by surfacing up the best stores for them. For searching and ranking, when a customer searches for something like Italian food, we want to show the most relevant results and rank them in the respects of distance and quality. We don't want to show restaurants that are 40 or 50 miles away.
At the bottom, you see a promotion from particular merchants. For merchants, we would like to help them to maximize their sales by providing targeted promotions of certain items at certain time of the day. Onto the order checkout. Now, at the top, based on the items on the shopping cart, we make recommendations about the side dishes that often go together. For example, if you order hamburgers and forgot to order fries, we'll make sure to recommend that. At the bottom, this is related to fraud related use cases. Like most internet e-commerce companies, we need to deal with a financial transactions and a credit card related issues. For the dispatching step, this is where the logistic engine kicks in. It sends the order to the merchants and then signs the order to Dashers. The engine's primary job is to dispatch the right Dashers at the right time for the right delivery to maximize the marketplace efficiency.
In terms of the food preparation time and the travel time estimate predictions, they have a few interesting nuances such as traffic, weather, whereas we know we don't have much control of. Another interesting challenge in this area is, unlike Amazon orders, food orders emerge on the fly and the food quality plays an important role in the delivery, like the extra five minutes could be the difference between a great meal versus an awful meal. For the last step, delivering the order. Regarding Dashers, the first step is how best to spend the right amount of money to bring on new Dashers on to the marketplace. Once they're onboarded, how to mobilize them to come out on certain night of the week or position Dashers at the right location to satisfy the demand on the marketplace. Now, in terms of the delivery assignment, we'll average machine learning to come up with optimal matching of Dashers to deliveries with these goals in mind, to ensure Dashers get more done in less time and consumers receive their orders quickly.
The last use case is once the food is delivered to the customer's doorstep, we would like to verify the accuracy of that delivery. Now, let's move on and talk about our machine learning platform journey. Our journey started about a year ago when DoorDash decided to invest in building a centralized platform. As usual, different ML teams have slightly different needs, so it is very important to establish the buy-in from those teams on the idea of a centralized platform. One of the ways to do that is to have a representation from each of those teams, as well as an open forum to voice the collective needs, opinions, and also be open to the contributions to the development of the platform. The collaboration model we establish includes machine learning council, that is responsible for making cross-functional team decisions such as bill versus buy. It also acts as a glue, which holds these different teams together so we can collaborate closely and learn from each other. Now, we don't claim this approach is the only right one, but the one that works for us so far.
These are the pillars in our platform. They are modeled after the ML [inaudible] process. It starts with feature engineering and then all the way to model predictions. The approach we use in building these pillars starts with thinking big, by working backward from the customers to put together a product vision document. Then, building these pieces out incrementally based on the needs of a community while keeping an eye out for the long-term vision. Now, I will share some technical details about some of the pillars.
Start with feature engineering. They're historical and real-time features. The historical features are generated from the Snowflake Data Warehouse in DataLake. The feature service is responsible for uploading billions of features onto the feature store. Then, for the real-time features, we build a framework on top of Apache Flink to generate this type of features, which are also written to the feature store. For model training and management, while in the exploratory stage, data scientists have the flexibility to train and gather the models. But before deploying those models to production, those scripts need to be checked in ticket, where our model training service pick them up and train the model, and finally store those model into a model store. We leverage the compute resources from the Databricks hosted platform to train our ML platform models. We also leverage ML flow such as tracking experiment runs, training results, compute resources, library versions and such.
Finally, the model prediction pillar, where we have a centralized online prediction service that's based on Kotlin and C++. It supports tree base as well as [pothos] models. And these models can be deployed easily via a runtime configuration. The prediction service can fetch features from the feature store if they're not provided in the prediction requests. For the prediction results, I'll log into data lake for data scientist to analyze and train the models. The next section, we'll cover interesting challenges with supporting high QPS prediction use cases. Now, I would like to handover to Arbaz to talk about an interesting scaling challenges of our online predictions.
Arbaz Khan: Thanks again for a comprehensive overview of the machine learning platform at DoorDash. Hi everyone. My name is Arbaz and I am excited to share this journey of scaling online predictions. Just a quick word of why it's critical to consider online predictions in an ML platform for scaling. In the graphics that you're seeing before, there were two kinds of workflows, online and offline. The online predictions act as a bridge between practicing ML and applying it to the real world use cases. When you go close to a real world application, that's when stakes get high. System failure in online where it could mean anything from a slow degradation to a complete website take down. That's why it's important to address scaling needs and reliability of online predictions much more than the offline ones.
When people think about scaling online predictions when starting in this domain or new in this domain, we ask this question that isn't scaling online production same as scaling any other microservice, if you're considering a microservice-based architecture? The answer is yes and no. Yes, because of prediction service. It lives in a microservice framework. It's implemented as a microservice. It does these database operations, but it has these unique challenges that require a specialized treatment. Let me enumerate these challenges, so that you get the understanding here. First, [inaudible] prediction requests have larger payloads. It is because each of these requests have dozens of features that need to be passed in. It's also a common practice to batch multiple prediction requests into a single request, because at times you're not just making one prediction. You're making multiple predictions for rendering a single page, for instance. As a result, the payload size increases. It could be anywhere from tens of kilobytes to hundreds.
Second, you need to make model computations, which are intense, but are still within the same [inaudible] constraint, as any of the microservice we're having. Third, there's one interesting way how prediction workflow is different, is that apart from production traffic, there's also experiment traffic. A data scientist and machine learning engineers, they keep running shadow test or AB test to validate their models, which adds a factor of traffic into the overall consideration. Finally, auditing. Not everyone needs auditing for re-ensuring compliant purposes. But here, it's even more important because here, a data scientist would use the auditing logs that is the outputs that come out of the prediction request to see if there are any issues in a deployed model in real-time. They will also use these logs to further train their models, which is a critical need for improving their models.
You would ask here that yes, I understand that there are these peculiar challenges you have to face when building. This is related to prediction, but what do you mean by scaling online predictions on this context? I would say that scaling can be best understood here from a user perspective. What are the users here? They are data scientists, machine learning people, and the systems, that is the microservices, the upstream microservices that would call your prediction service. The data scientists will keep pushing newer models and newer features into production. They will also keep experimenting rapidly, which is, as I said before, it adds to that traffic. It sort of multiplies the overall traffic by a factor goals for scaling and does the microservices.
These are these other sorts of upstream services that would call you and want you to reliably support higher and higher QPS because the product is growing or because there is a situational uptick in demand, either because there was this event that was happening. In our case, DoorDash last year with the pandemic, which was causing a boom for delivery industry. It was also the ideal event for us, which increased the exposure and caused a situation, uptick and demand.
When you approach these needs of scaling, I believe you go through these different phases of scaling and the increasing order of intensity. Then, the initial phase, as we roll out the system, the traffic picks up. You are in this happy phase where you could just throw more instances, more workers and shards and get the flow going as more traffic comes in, and the system will oblige happily. Then, we'll come phase two, where we would see that horizontal scaling has its limits and we have to see that there are these users that are consuming more resources than the others. To be able to continue horizontal scaling, they have to be isolated out. After that, we just resume scaling. Resume scaling until we can't anymore, because either our costs are shot through the roof, or there are inherent deficiencies in our system and so there is a need to optimize to survive.
Then finally, since everything would not be in your control, once your services grow big enough, like really big, things that you couldn't control, such as the complete infrastructure, we'll start seeing major negative effects. You will be just left monitoring all infrastructure and this might require a revamp of sorts or force [throttling] to survive at that stage.
Now, my goal with this stock is to make you live these four phases through our experience that we had at DoorDash. I want to share what precise scales did we have these phases, and what did we do to transition from them? First phase, the happy phase, the horizontal scaling. Let me just chalk out the essential components that will be involved here. The feature stores that are discussed for us. It's deployed Y AWS elastic cache to be able to horizontally scale, we can just like increase shards on this cluster. The prediction microservice, as Hien mentioned before, it's a Kubernetes based microservice. It's elastically scalable, can add more service replica to it. The prediction store it's a snowflake table, we partition it by multiple relevant fields, such as date, model ID. And then the link between the service and the prediction store is mediated via a stream processing to ensure throughput. And we have a stream processor that can elastically scale X number of workers as the traffic goes through that bridge.
So, we were able to use this whole set up of horizontal scaling to add up to a system and handle more and more traffic. As you can see here, this graph shows, how did we start turning upwards of 15,000 predictions per second by June of last year, starting at 1000 predictions per second on March, was just like within a span of three months, grown 15 X here, all on the basis of just like adding these workers, shards, or whatever. And then we observed that there are at least some models that consume more resources than the others. So just like pulsar table here for B 95, latencies, Dasher dispatch, or like one of the highest latency models is it's 50 to 60 X, more than the lowest latency models. So you can see that there are more resources consumed by this high latency model and the low-latency models start getting cannibalized.
They will start seeing these degradation and their SLES. So even though you're adding workers to the horizontal scaling cycle, but the degradation does not get any better. So, that's when we transition. That's how we transitioned to phase two at this point. When we realized that user isolation is the need of the art, and we start to think that what could be the strategies of isolation that we can employ here? One is the thing that we were using that we have all models live within once service deployment. It works when models are homogenous in size, and the usage patterns for these models are very similar, which is not often the case. And so there's this other approach that you can have one service deployment per model. Here the concern will be caused primarily, because to be able to work in a cost efficient manner, you need some sort of order scaling in place because every time you have the service running, hosting a model, if their model is not getting used, you are wasting the CPU hours.
Auto scaling can help reduce that but it needs to be working perfectly for you to get any cost efficient system on there. The middle down here is a hybrid approach where some of the models can live within one single deployments, and you consciously made the decisions as to when to accelerate. So there are multiple service deployments. Each service deployment can host multiple models. You would ask that, is this the way to go always? Whether, it does seem like it's best of the both worlds. In terms of extracting the cost, and it's also mitigating bottlenecks. But the drawback here is that we need to be able to tell when to isolate here, and once we have isolated, we need to have an ability to take in mind that which models live there.
Assuming that we were able to go past these drawbacks, or like you were able to have the system in place. You'll probably ask that how far did this attempt take you in terms of your scaling endeavors? Was it a happy scaling life ever after? No it was not. We were able to grow a big enough number though. We hit six figures in big predictions per second, but we couldn't go as much higher as we had wanted to with this. We couldn't avoid a shard here to be the limiting factor. But one benefit that we got was that not every use case out there in our system was impacted because of one child being old. Where it was a box situation, though, regardless for the limiting shot, it started growing much faster than the others. We started hitting AWS hardware limits. We couldn't scale horizontally any further for this particular shard, but we still needed the predictions to keep flowing.
And that's when we enter into the phase threes, until at this point, scaling was merely doing more work, but to hit for the larger scales in a sustainable manner, the mission was to do more with less. So it's like doing more work with less resources. Most common example that I find here is a case of imperfect load balancing. Imagine you're adding workers, but because traffic is not evenly balanced, you're not shedding load off your existing workers. And the [inaudible] workers will continue acting as bottlenecks no matter how many modes you add. So optimizing load balancing here in this case is how you would scale to survive.
So how did we holistically approach this? Was that we did use multiple optimizations [inaudible] layers and the microservice layer, the feature stroller, and the approach here was to harvest a loop of observations and iterations. So for the microservice, an observation could look like you're doing load testing, understanding those bottlenecks of how far the QPS can go before it starts breaking. You start to see also what are the latency profiles? What are the places where I'm spending the most time on? GRPC dressing was one such tool that came out helpful to us. We published on that as well in our blog. And we use these observations, the platform to make iterations. There were these low-hanging fruits that we could readily adopt, such as the parameter tuning, like what parameters could be [inaudible]?
These could be that, what are the sizes of our payloads? If we are batching much more than desired, we can cut the bat sizes, or if you're not making use of the resources much, we can increase the bat sizes. A little certain gutter with their timeouts on connections and what we'll expect. And then there were these bandwidth optimization that we can think of either disabling them or enabling them. And there was few more involved optimizations, which would payload compression, compile optimizations, and all of these in together helped us improve the microservice layer. And then it runs onto the feature store layer, try to see that the feature store that we had at that time, this cluster wasn't the best that we had.
We did some benchmarking with our desk basked store to see that if a desk based store can help us cut our costs or help us get more performance out. But this benchmarking helped us understand that our latest cluster approach was the fastest and the cheapest one. So we had another option, but to optimize at the top of the existing implementation. So we took this approach of a complete scheme model design and see how can we make use of [inaudible] in a better way, in a more performant way. Results were in line with that. We reduce our cluster costs by 3 X, and also increased the performance by some of these optimizations. Again, this is also published in one of our blogs.
So all these efforts we bridged 1 million predictions per second, the magic number, and the system was running stable all this time. This was a major accomplishment for us, because we would use the setup for quite some time. We were able to hold up traffic, even up to 2 million predictions per second. It was not obviously the end of scaling at times, we were at the cusp to explore what is anything and anything can happen.
This is where we transition into the final phase that we're going to discuss. It's the phase at which we have started hitting scales that could impact the entire production infrastructure. So think of implications such as bringing all of the services down at once, because your service deployment went haywire. These are the kind of implication that can happen when service grows that big. Just like enumerating furthermore, that we encounter a logging and metrics, infrastructure stopped supporting us because we were hitting these limits that were not acceptable. Third party, when a segment for processing blocked us for sending too many events because we breached the rate limits there as well. In-house service discovery module console threatening the total outage.
When one of our service deployment went on continuously start loop for the parts where they were constantly sending, I press update requests to console, which was also helping out the other services. And because console was over in the other services were getting impacted. So yeah, this is how we tackle all of them, we just pick them one by one. For the logging and metrics, we got them to a bare minimum, just log whatever we need at the bare minimum level. For metrics, we moved from a pull-based monitoring framework to a push-based monitoring framework to cut the cost, to cut the number of events we were sending. We migrate away from a third party streaming window to an in-house Kafka solution to have more control on the deployments, the cost and the rate limit. We reduce our total number of pods to ease the load on a service discovery by making them bigger.
So instead of having a much larger number of pods, you have smaller number of pods, but a bigger in size. With this conscious and sample use of resources, we were able to hit much larger scales north of six million predictions per second. Although this wasn't everything that we had wanted for, there were more scaling that we anticipate to come, and we're keeping an eye out on some of the bottlenecks here as well. We'll keep publishing or docking as we approach these higher and higher scales.
So if you were to ask me and all this information that I've shared so far, what are the three key takeaways. First, is that there should always be this understanding of when do I select. We took the approach of isolating use cases wherever possible. And then secondly, every time we faced a new scaling challenges, one easy way out is to scale out. That is just keep adding workers, keep adding norms. It usually helps. Especially in situations like Friday evening, there's high-traffic, there's QPS increase, nobody's… let's just scale-out. But if you're constantly seeing more traffic than you originally planned for, it's time to introspect and see how can you make the system better?
Because if you don't, it will start to show up somewhere. The worst case, it will show up on the company's infrastructure and it will not about just your service but all the services out there. That was my part. Thanks for patiently hearing that, I will hand it over to Hien for carrying you through the last leg of this presentation.
Hien Luu: Wow. Thank you Arbaz for walking us through those fascinating challenges, and how we tackle them. Here we will like to share a few lessons learned. For happy path, as Arbaz mentioned, the prediction service is a part of the user of production traffic flow. So ideally it shouldn't cause an outage if something went wrong by fetching features and such. So therefore it is very important to have a proper default values in place. In terms of customer obsession, in addition to work with them very closely with ML teams, we also strive to understand the business problems they're trying to solve. As well as the business impact. By having this understanding, it helped us in terms of the prioritization of our projects. For the last one big vision, and building incrementally. By building out our platform incrementally, it gives us the opportunities to receive feedback and also have to make the necessary adjustments along the way. And this has been really helpful.
In terms of future work, there's a lot to do, but here are a few that we are considering. For micro service optimization, as definition, volume increases, and we expect that to happen in the near future, while looking into the potential caching, the feature values, as well as prediction results. I'm sure there'll be many interesting technical challenges in these two areas. We've already in generalized model serving. So far most of the ML use cases are based on structured data. Moving forward, though, we'll see more use cases in the MLP and image recognitions. So therefore, we need to extend our platform to support those model types. And lastly, the unified prediction client, as the ML platform adoption increases, we want to provide an easier and optimized way of making prediction requests for our customers.
So, thank you for attending this session. And here's a link to the DoorDash engineering blog website, where you can find many interesting blogs about data science and machine learning.
Hien Luu
Hien Luu is a Sr. Engineering Manager at DoorDash, leading the Machine Learning Platform team. He is particularly passionate about the intersection between Big Data and Artificial Intelligence. He is ...
Arbaz Khan
Arbaz is a Machine Learning Platform Engineer at DoorDash where he focuses on challenges around usability and scalability of online model serving. He has been directly involved in growing the scale of...


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During the time I was the membership chair of a wonderful, small synagogue, I wondered why prospective members did not join, or chose to wait months before joining. Now that I have relocated and am myself a prospective member, here is what I have learned from considering 6 communities, choosing one and visiting – so far – 4 synagogues there (each of them multiple times for reliability purposes).
Getting new members is a team effort.
The Membership Team should consist of the webmaster, the front office staff, the rabbi, the membership chair, the president , the entire congregation, and the treasurer, in that order.
Some of the information below may seem obvious. I am mentioning these points because, to one and usually to several congregations, they were not obvious.
The first place a prospective member turns is usually the website.
The member wants to know if there are services and what time they are. That should immediately be obvious, with ideally no more than 2 clicks on the site.
To make it even more useful to prospective members, ask new members who are also new to the locale and are different ages what changes they would suggest making in the website.
The site needs to be kept up to date; otherwise, it can become a history lesson in what happened a previous year (sometimes many years ago).
The "about us" buttonand the "contact us" button should be obvious. The descriptions of the synagogue can welcome or turn off a visitor.
Putting information and links on both general and Jewish aspects of a community on the website is a wonderful extra! Many people consider a synagogue/Jewish community before they consider moving to the locale.
The front office staff needs to be very welcoming and have at least a rudimentary knowledge of the community and some common synagogue-related words, such as minyan and yahrzeit.
The front office staff should be welcoming and encourage a prospective member to meet the rabbi. If the rabbi is so busy that he doesn't have time to even meet a prospective member, the rabbi likely doesn't have time if the member has an emergency or needs counseling. Even if the visitor does not choose to meet the rabbi when it is offered, s/he will remember the tone of the front office staff's greeting.
When prospective members e-mail or call the rabbi, the rabbi needs to respond – enthusiastically and quickly. The e-mail can be brief. The rabbi who responds at all shows interest, which in itself is an unusually strong selling point.
The rabbi's sermon needs to have a clear beginning, middle and end – and a "take home message" that is relevant to the congregation. The Shabbat morning davening, especially, needs some energy.
Larger synagogues try different approaches to encourage members to greet visitors. Their problem is that with so many people, it is hard for members to know who the prospective members are, especially at a simcha such as a bat mitzvah.
One tried having visitors stand up at the end of services and introduce themselves.
Curious as to whether this worked, I tried it – and almost no-one approached me. (It would work better at small synagogues for large bar mitzvot and Break-the-fast kiddushin.) Another synagogue tried this with the "yellow cup" method. The color of the cup makes no difference; this method doesn't seem to work.
I next tried wearing a small pin decorated with my first name that someone had given me as a child. People appreciated the fact that they could read my first name; it started some conversations at two shuls I visited and people even remembered me the next time I went there because of the small name pin.
Pre-made name badges on cords kept at the synagogue by the front door were a strategy of another synagogue, so visitors knew members' names. Only a few members were wearing them, however.
It may be possible to combine these by typing some name badges in advance, of common first male and female names, for visitors. Ushers should encourage people to wear them. These name badges should be a different color than those for members, so people can differentiate them.
If the membership chair isn't there, the rabbi or the pulpit officer needs to welcome prospective members warmly, and stay with them for at least part of kiddush, introducing them to other members. The "hi and bye" approach is better than nothing, but not by much.
If there are ushers for a larger service, they should be asked to watch for, and welcome, newcomers. Their friendly greetings and introductions to someone sitting down whom the newbie could sit with will be remembered. Obvious signs that someone is a newbie – such as carrying a coat because they don't know where the coatroom is, asking directions to commonly known rooms, and asking what the usher does and doesn't like about the shul – are clear signs that the person is shul-shopping! Ushers need to be told this, and to know to ask the person's name, welcome them and alert a membership chair or president that a prospective member is there.
Some synagogues have monthly calendars of activities out by the front entrance to the sanctuary or the front door, to encourage people to get involved. They may also have transliterated Friday night prayer books (synagogue-made). One shul had transliterated many prayers from Sim Shalom and glued them over the English in about 10 siddurim, so people who wanted help would not feel obvious about this. These special siddurim were marked with blue tape on the spine and put in a particular bookcase by the door of the shul.
The membership chair needs to follow up with the prospective member every 1 – 2 months, as hard as this may be. The follow-up is not "When will you join?" but "How are you doing? How can the congregation help you find resources, etc here? Can you come to our X event?" Putting the newbie on the e-mail list is also an option. The prospective member should know that someone from the synagogue knows his name.
Make newbies feel valued by asking about favorite activities they enjoyed from their previous synagogues; you could get some great ideas that way! To cue their memories, ask the name of their last synagogue during a phone conversation and look it up online, and then ask them about a few activities (if they were active). Also ask if they had a leadership role there; they may be a future leader at your synagogue with encouragement by various people.
Try to connect prospective members with others who have similar interests, from hobbies to careers. Have the member invite the newbie to the shul or to a fun event elsewhere. If there is a New Member Shabbat later, have the current member introduce the newbie (if the newbie has decided to join).
The congregation needs to show real interest in prospective members, from the warm greetings and smiles when they walk in, to sitting with them at services, to talking with them afterwards and at the oneg/kiddush. Everyone should be an ad hoc member of the membership committee! This is easier to do in small synagogues, where strangers are immediately obvious when they walk in the door.
Look for prospective members in less obvious places, such as the kosher section of the supermarket or the Jewish books section of the bookstore. Newcomers may start there and then go on to find a shul.
Another great place to look for members is in newcomers' and singles groups, such as Jewish "meet-ups." Newcomers to town often visit these groups and may ask about synagogues. At one such event, two attendees had a lively discussion about their (mostly) negative experiences at various local shuls each had independently visited. Sitting in between them was another person who was apparently on the board of one of the synagogues; he was stunned to find out about the newcomers' negative experiences there. Another newcomer had had a positive experience at a local synagogue and had offered to take the others to her new shul to introduce them to people.
Prospective members are looking first for a sense of community, of fellowship, and then of spirituality. They want to know they matter to someone at the synagogue, especially if they are new to town. Greet them like they are special people; don't ignore them like they are wearing invisibility cloaks! Then visitors want to know if they like the rabbi's approach to the service. If the person returns a second time, pour on the smiles and greetings even more – the person is checking to see if the congregation is consistent and wants a relationship.
Then someone should call the prospective member and personally invite him/her to a specific event at the synagogue. Whether or not the offer is accepted, or even whether someone answers the phone, the personal call or phone message shows caring.
Another concern of many prospective members is the cost of joining, at a time when moving and settling-in expenses may be very high. When the topic of joining is raised, there should automatically be a mention that reduced fees are available if needed and are on a confidential basis. People will not ask for these unless they need them. Wait to invite someone to join until they have been coming for a while, are greeted by name when they come in the door and feel comfortable in the community.
When a member joins, get him involved right away in the activity of his choice, if he is willing. This gives him an opportunity to meet people and make some real connections. Some synagogues ask prospective members what groups they would like to join and then forget to invite them. Other synagogues have teams of members who do certain activities together, but the teams are formed in the summer. People who join after the summer need to wait a year before joining, unless they personally ask to join and make a real effort.
Make a special effort to get single adults involved. Many synagogue activities are designed for couples or young families. Single adults may feel left out, even if it is unintentional on the part of the synagogue. Have a service or oneg primarily for singles, or find some other way for them to meet each other.
The prospective and new member is a lot like a seedling. It needs attention and care, to feel like he or she is part of a community and matters to people. Will you be the person who "waters" the seedling, or the one who lets the prospective member's interest "dry out"?
Katie Schwartz, the director of Business Speech Improvement, is a former synagogue membership chair. She relocated to another state and experienced what prospective members go through, contacting and visiting new synagogues. Ms. Schwartz has written 4 books and many blog posts and articles.