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a new casino for Connecticut, good or bad?
June 8, 2017 By dduane in Casinos, economics, local casinos, microeconomics, Uncategorized Tags: Casinos, Connecticut casinos, economics, securities analysis	Leave a comment
Shortly after I retired as a portfolio manager, I went to work part-time at the Rutgers business school in Newark. No, it wasn't to teach investing or portfolio management–accreditation rules effectively rule this out for anyone without a PhD in (the alternate reality of) academic finance. Instead, it was in a practical management consulting class run by adjuncts with real-world experience and advising mostly small businesses. (We were all fired several years later and the program–the only profitable area in a school dripping red ink–dissolved. …but that's another story).
Anyway, one of the projects I mentored involved a casual dining restaurant. A student had a connection with a very successful pizza restaurant whose approach might serve as a model for our client. The pizza owner said he had superior results. How so? …he had cloth tablecloths and fresh flowers on each table; the food was good; he spoke with every customer himself to make sure everyone knew they were welcome. In fact, he drew customers from as far as 15 miles away.
How far was the closest competing pizza restaurant? …30 miles.
Put a different way, in this state customers hungry for pizza went to the closest restaurant, despite what this owner thought was his special charm!
It's the same with a lot of other things, including local casinos.
In the case of Connecticut, the two existing operators are coming under threat by the decision of Massachusetts to legalize gambling in that state. In particular, it's allowing MGM to open a casino just on the northern border of Connecticut in Springfield, MA.
Hartford has just responded by authorizing a new casino in East Windsor just on the Connecticut side of the border from Springfield, to be jointly run by the two incumbent operators.
This is an interesting case. Let's take a (simple) look:
My pizza rule says customers go to the closest casino. If that's correct, the new Massachusetts casino will reduce the existing Connecticut casinos' revenue by a substantial amount. Hartford estimates that amount at a quarter of the current business, about $1.6 billion. If they want to keep the remaining 75%, however, it seems unlikely to me that the casino operators will be able to reduce their costs by much. So their profits could easily be cut in half.
And when the proposed East Windsor casino opens?
Figure that East Windsor will take back from Springfield half of the revenue initially lost. That's $200 million a year. From the state's point of view, any revenue gain means higher tax collections–in this case, about another $35 million a year. So it's understandable why East Windsor has gotten a legislative seal of approval. It's not clear, however, that the casino operators are going to be better off–because they're taking on the expense of a third location in order to protect 12% of their current revenue.
We've also seen this movie before in the northeast US, with the effect on Atlantic City of gambling legalization in Pennsylvania, and on Pennsylvania of legalization in Ohio and Maryland. One additional complication in this instance is that both of the incumbent operators are Native American tribes, for whom maintaining/expanding employment may be more important than profits. A second is that the new CT casino will be run by two in-state rivals. That should be interesting to see.
Disney (DIS): the valuation issue
February 10, 2017 By dduane in Cable, earnings surprise, Entertainment, securities markets, strong earnings, Uncategorized Tags: DIS, Disney, ESPN, securities analysis	3 Comments
Long-time readers may recall that I became interested in DIS in late 2009, the company acquired Marvel Entertainment, a stock I held, for stock and cash.
I hadn't looked at DIS for years before that. I quickly learned that DIS was a conglomerate, that is, a type of company where the most useful analysis comes taking the sum of its constituent parts.
I knew the company's movie business had been struggling for some time and the theme parks were being hit hard by recession. Still, I was more than mildly surprised that ESPN (plus other media that we can safely ignore) made up somewhere between 2/3 and 3/4 of DIS's operating earnings. Why did they still call it Disney?
Given that the parks are a highly cyclical business and movies moderately so–meaning the PE applied to those earnings should be relatively low–and that ESPN was showing all the characteristics of a secular growth business–meaning high PE–I thought that ESPN represented at least 80% of the market capitalization of DIS. (That's despite the fact that the market would apply a higher than normal multiple to cyclically depressed results).
So DIS was basically ESPN with bells and whistles.
ESPN's turning point
In 2012, ESPN made a major effort to enter the UK sports entertainment market. To my mind, this wasn't a particularly good sign, since it implied ESPN believed the domestic market was maturing. Worse, ESPN lost the bidding, closing out its path to growth through geographic expansion.
It seemed to me that DIS management, which I regard as excellent, understood clearly what was happening. It began to redirect corporate cash flow away from ESPN and toward the movie and theme park business, which had better growth prospects, and where it has since had unusually good success.
2014-16 results
Over the past two fiscal years (DIS's accounting year ends in September), the company's line of business results look like this:
ESPN + revenues up by +11.9%, operating earnings by +6%
parks revenues up by +12%, op earnings +24%
movies revenues up by +30%, op earnings +74%
merchandise revenues up by +4.6%, op earnings +33%.
the valuation issue
ESPN has gone ex growth. This implies these earnings no longer deserve a premium PE multiple. To me, the fact that ESPN now treats WWE as a sport (!!) just underlines its troubles.
The other businesses are booming. But they're also cyclical. So while improving efficiency implies multiple expansion, earnings approaching a cyclical high note implies at least some multiple contraction.
Because the two businesses are so different, I think Wall Street is making a mistake in treating earnings from the two as more or less equal.
DIS will most likely earn $6 a share or so this fiscal year. That will be something like $3 from ESPN and $3 from the rest.
Take the parks… first. Let's say I'd be willing to pay 18x earnings for their earnings. If that's the right number, then these businesses make up $54 a share in DIS value.
Now ESPN. If we assume that the worst is over for ESPN in terms of subscriber and revenue-per-subscriber losses, we can argue that the future earnings stream looks like a bond's. If we think that ESPN should yield, say, 5% (a 20x earnings multiple), that would mean ESPN is worth $60 of DIS's market cap. If we'd still on the downslope, that figure could be a lot too high.
$54 + $60 = $114. Current stock price: $109.
my bottom line
My back of the envelope calculation for the parks… segment may be a bit too low. I could also be persuaded that my figure for ESPN is too rich, but it would take a lot to make me want to move the needle higher for it.
Yes, most of DIS's earnings are US-sourced, so the company could be a big winner from domestic income tax reform.
But if I were to be holding a fully valued stock on the idea of a tax reform boost, I'd prefer one with more solid underpinnings. At $90, maybe the stock is interesting. But I think ESPN–the multiple as much as the future earnings–remains a significant risk.
casino gambling in Japan
December 19, 2016 By dduane in Casinos, Japanese casinos, Uncategorized Tags: casino gambling in Japan	Leave a comment
Last week the Japanese Diet approved the creation of the first Las Vegas-style casinos in Japan. Most Japanese citizens appear to be indifferent, but introducing a new form of legal gambling has been a priority for the political establishment for some time. Although the standard rationale for legalizing gambling anywhere in the world is to take money away from the local underworld, the main reason in Tokyo seems to be to attract more foreign tourists–and keep them in Japan longer.
It will be a while before gambling consortia of construction companies and resort developers, presumably with foreign partners providing the expertise, are formed and for the precise rules about allowed games and taxation to be articulated. But two issues are already clear:
–over the past ten years or so, there has been a dramatic expansion of the casino business in Macau, as well as in Singapore/Malaysia, the Philippines, Korea and, to a lesser extent, in Australia. At some point, the market in this part of the world will become saturated. Then, the fact of gambling won't be enough. The quality of the operations will count for a lot more. So I don't think Japanese casinos are sure-fire winners. We have seen this already in Macau, where SJM, once the monopoly operator, continues to lose market share. Will Japan push the region as a whole into maturity?
–who will run the casinos? Presumably Wynn, Las Vegas Sands and Galaxy Entertainment will all be interested. My guess is that groups including each of the three will be the key contenders for licenses. Will each get a license? Will that be a good thing? Will Wynn Macau and Sands China be involved? Certainly, Galaxy will be, since the Macau operations and the main company are the same. In the other cases, it's not so clear. If investment capital were no object, my guess would be no.
Macau and ATM machines
December 9, 2016 By dduane in Casinos, Uncategorized Tags: Casinos, Macau, Portfolio management, Unionpay	1 Comment
During its days as a Portuguese colony, Macau was reputed to be a key center used by the mainland underworld to launder its ill-gotten gains. The main laundromat, as it were, was allegedly the collection of casinos run under a monopoly granted to the Ho family.
After the return of Macau to Chinese rule, the government moved quickly to break the monopoly and to guide the casino industry toward the Las Vegas model through technology transfer by granting casino licenses mainly to prominent US Las gambling operators with a Disney-esque approach to business.
A second political problem threatening the legitimacy of the Chinese Communist Party began to arise during the last decade as fabulously wealthy political insiders began to flaunt their riches through elaborate, ostentatious gambling jaunts to Macau. A crackdown ensued, which also served the long-term interests of Macau by strongly redirecting the emphasis of the Macau gambling industry away from high-roller VIPs toward middle class and upper middle class patrons. This, by the way, follows the development of the gambling market in the US.
During US trading hours yesterday, media reports from Hong Kong surfaced suggesting Beijing was beginning to crack down on middle class gamblers as well as VIPs. The stories said the daily limit that mainland residents vacationing in Macau are allowed to withdraw from their (renminbi-denominated) bank accounts through a local ATM would be cut in half from–MOP 10,000 ($1300) to MOP 5,000, effective tomorrow.
Given Beijing's plans for Macau's economic development, this report made little sense–although, realistically speaking, who knows what Beijing's day-to-day thoughts are.
The US-traded Macau names immediately dropped by around a tenth in a flattish market. In today's Hong Kong trading, Macau gambling stocks fell by 7% or so (expressing about half the negative sentiment in NY)–with the strongest (relative) performance by Ho-controlled SJM.
After the close of Hong Kong trading, mainland authorities "clarified" the initial report, saying that, yes, the per transaction limit on ATM withdrawals by mainlanders in Macau was being cut in half, but that the total daily limit would remain unchanged. No reason why the clarification took 12 hours to be made.
In early US trading today, Macau-related stocks have made up about a third of their losses from yesterday.
As a holder of Wynn Resorts, Wynn Macau and Galaxy Entertainment, I'm going to sit on my hands. If I held nothing, I'd be inclined to buy a bit. My preference would be for the Hong Kong names, however, for two reasons: the US market is being driven now by dreams of a domestic industrial revival, so foreign casinos aren't at the top of institutions' wish lists; and investors who dumped out their Macau holdings in a panic yesterday will be loathe to buy them back at a higher price, at least for a while.
developments in Macau gambling
November 8, 2016 By dduane in Casinos, Macau, Uncategorized Tags: Macau casinos	Leave a comment
Today's Election Day. Be sure to vote.
I think the results of the presidential election may have far-reaching effects on the US economy and stock market, with a Trump victory being especially bad. But that's a topic for another day.
There are three recent developments of note in the Macau casino gambling market, one whose recent decline I badly underestimated:
–after peaking at a monthly gambling win of MOP 30.7 billion (US$3.8 billion) in February 2014 and beginning negative year-on-year comparisons that June, the market finally bottomed in June 2016. Win for that month was MOP 15.9 billion (US$2 .0 billion); yoy comparisons turned positive in August. An obvious plus.
–18 marketing employees of Australia's Crown Resorts were detained in China last month. Their crime appears to be offering., while in China, larger-than-permitted inducements to Chinese high rollers to visit Crown Resorts properties in Australia. Since Crown also has casino operations in Macau, however, it's not 100% clear that this is the issue. A similar situation occurred in 2015, when marketers for casinos in Korea offered illegal inducements in an effort to get high rollers to visit. For the moment, at least, this has marketers for Macau erring on the side of caution.
It's unclear whether this is good or bad for casino operators. Running VIP gambling operations is all about controlling the cost of the rebates and amenities needed to get high rollers to commit to visit a casino and gamble a specified (large) amount. In the past, Crown has initiated price competition in this arena. At one point, the Macau government stepped in to set a cap on allowable rebates. Whatever Beijing's motivations, it may be having the same effect now.
–profits at existing Macau casinos generally have been surprisingly strong. That's both because of non-casino attractions like restaurants, shows and shopping, and the continuing strong evolution of a non-VIP market. Two exceptions: the Ho family legacy casinos and companies like Wynn Macau, which are opening substantial new capacity. Contrary to what one might expect,new offerings are not immediately drawing gamblers looking for novelty. Instead, the Macau experience has been slow but steady progress to full utilization, achieving that goal, say, a year after opening.
One other point: Macau has been marching to its own drummer in stock market terms over the past couple of years, driven by the course of casino win. That's likely to continue …but it will probably be a positive rather than a negative in 2017. So Macau may act as a refuge in a time of choppy global markets.
(Note: I own shares in Wynn Macau (HK1128) and Galaxy Entertainment (HK0027), as well as a tag end of a former Sands China (HK1928) position that I've mostly sold (and am not inclined at present to rebuy.))
Disney (DIS) from 30,000 feet
August 10, 2016 By dduane in Cable, earnings conference calls, Entertainment, ESPN, Uncategorized Tags: company analysis, DIS, Disney from 30000 feet, ESPN	1 Comment
I'd only followed DIS from afar until the company acquired Marvel Entertainment, which I held in my portfolio, for a combination of stock and cash in late 2009. I kept the shares I acquired and also bought more while DIS was depressed by critics doubting the wisdom of its move. I'm tempted to write about how wrong that view was, but that's for another day (not soon).
As I studied DIS's financials, I found that ESPN accounted for about 75% of the firm's overall operating profit. The movie studio, run by a former monorail driver at the theme parks, was a mess. Income from the parks was depressed by recession. The Disney brand was also almost completely dependent on female characters, making Disney attractions less appealing to half the adolescent population. ESPN, on the other hand, was/is the dominant sports cable franchise in the US and was going from strength to strength. For a moment–until I realized the marketing advantages of having the Disney name in the public eye–I wondered why the company didn't just rename itself ESPN.
In addition, the simple percentage of earnings seemed to me to understate the importance of ESPN to DIS. The movie business is typically a hit-or-miss affair and therefore doesn't merit a premium multiple. Same with the hotels/theme parks, because they have a lot of operating leverage and are highly sensitive to the business cycle. So I concluded the key to the DIS story was the progression of its secular powerhouse–and its one high-multiple business–ESPN. Nothing else mattered that much. (Of course, I didn't understand the full power of Marvel, or the turnaround in the Disney studio, or the subsequent acquisition of the Star Wars franchise, but that's a separate issue.)
In 2012, ESPN began an effort to expand its business in a major way into the EU by bidding large amounts for broadcast rights to major soccer games in the UK. Incumbent broadcasters, however, realized (correctly) that no matter what the cost it would be cheaper to keep ESPN out of their market than to deal with it once it had a foothold. So they bid crazy-high prices for the rights. ESPN withdrew.
ESPN's failure was disappointing in two ways. A new avenue of growth was closed off. At the same time, the attempt itself signaled that ESPN believed its existing Americas business was nearing, or entering, maturity. That's when I began easing toward the door.
The issues for ESPN–cord-cutting and the high fees ESPN charges–are very clear today. What I find most surprising is that it took the market three years, and an announcement of subscriber losses by DIS, for the stock market to focus on them. So much for Wall Street's ability to anticipate/discount future events, even for a major company.
I don't think ESPN is helping its long-term future by seeking to boost ratings by having personalities shout at each other in faux debates. Nor does covering WWE as if it were a real sport. I think they're further signs of decay. My sports-fan sensibilities aside, the real issue is about price.
Suppose every cable subscriber pays $4 a month to get ESPN, but only 15% actually watch sports–or would pay for ESPN if it weren't part of the basic package. If so, the real cost per user is closer to $30 a month, most of which is being unwittingly subsidized by non-users. There's only one way to find out if current users would be willing to pay $30 for ESPN, which is by removing the service from the bundle everyone must buy, reducing the basic cable charge by $4 a month, and offering ESPN separately. That's what the cable companies want–and what ESPN is looking to avoid.
We're nowhere near the end of this story. I don't think the final chapter will be pretty for ESPN.
On the other hand, as I see it, just after the UK rebuffed ESPN, DIS began to direct its ESPN cash flows away from cable and toward building up its film and theme park businesses. For me, this was the sensible thing to do. And it confirmed my analysis of the situation with ESPN.
My bottom line: for four years ESPN has been the cash cow that's funding DIS's expansion elsewhere. Wall Street only realized this twelve months ago. But DIS's reinvention of itself is still a work in progress. Until the market begins to view DIS as an entertainment company that happens to own ESPN rather than ESPN-with-bells-and-whistles the stock will continue to struggle.
Atlantic City casino gambling
May 23, 2016 By dduane in Casinos, Uncategorized Tags: Atlantic City, casino gambling, Casinos, casinos gambling in New Jersey	Leave a comment
For a couple of years I was an adjunct at Rutgers business school. I worked on a course where teams of MBA students provided management consulting services for actual companies. For one project, one of my teams interviewed a pizza parlor owner about the key characteristics of his restaurant that attracted business. He said: good food, extensive menu, fast service, friendly staff, tablecloths on the tables. These enabled him to get customers from as far as 7-8 miles away from his store.
How close was the nearest competitor? …15 miles away.
All of the attributes he named may have been important to get any customers to his restaurant, but it's hard not to think that distance is key to defining his market area. That's true of any generic bricks-and-mortar business.
…which brings us to Atlantic City.
That beach resort has had its annual gambling revenue cut in half since competing casinos began to open in neighboring Pennsylvania in late 2006. Additions in Maryland and Delaware haven't helped, either. The issue is the same as with pizza. Absent some incredible attraction (think: Las Vegas), the average gambler will typically choose the closest casino to patronize.
The response of government in New Jersey to the competitive threat has been quite odd, in my view. It hasn't been to build up the city as a resort destination or to improve transportation access. The main thing I've seen has been the attempt several years ago to help yet another casino, the Revel, to open, adding new slot machines and table games to a market already awash in overcapacity.
Potential good news is that, after the closing of four casinos (Atlantic Club, Revel, Showboat and Trump Plaza) in 2014, the market appears to have stabilized. Even online gambling is perking up, having brought in $16+ million in April (although this is still a far cry from the $80 million average monthly take the state had been touting when online was legalized).
The other side of the coin is that Trenton is again "helping" Atlantic City by opening the door to building two new casinos in northern New Jersey. Local voters will vote on proposals later this year. Maybe the idea is to stabilize the state's gambling tax revenue at any cost. But nothing seems to me more likely to snuff out a nascent recovery in AC than this.


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History, art and science of remote sensing.
Fundamental principles of electromagnetic radiation and energy sources.
Interaction of radiation with the earth's surface and atmosphere.
Sensors and platforms and idealized remote sensing system.


The BMW 1 Series' USP is its rear-wheel drive chassis, something no other small hatchback can offer, and a sign of the firm's driver-led priorities.
The BMW 1 Series' USP is its rear-wheel drive chassis, something no other small hatchback can offer, and a sign of the firm's driver-led priorities. But it was also one of the first true premium models in the segment, and has become a familiar sight on our roads. It's not the most practical, with a small boot and cramped rear cabin, but it has a wide range of engines and body styles to choose from.
Initially the 1 Series arrived as a five-door hatchback only, but was soon followed by a two-door coupé, three-door hatchback and a stylish convertible. None could exactly be considered practical, and the five-door has no more room than the three-door.
In fact, it's the Coupé that has the biggest boot - 370 litres rather than 330 litres found in the hatchback models. Fold the seats flat and you can increase this to a reasonable 815 litres. However, whatever model you choose cabin space is at a premium, so this is not a car for growing families.
As expected with BMW though, all engines offered are great units - whether it is the 2.0-litre diesel (badged as either a 116d or 118d) or the petrol 120i, 118i or 116i. The 116d or 118d are the engines to go for if annual road tax is a consideration, costing only €160 whichever you choose thanks to the EfficientDynamics technology fitted across the range.
Trim wise you're likely to be able to choose from ES and SE specification, the former being a more basic offering. And both automatic and manual gearboxes were available, though the self-shifter is rare on the used market. Thankfully the six-speed manual is an excellent unit in itself.
It's the 118d that is the most appealing, thanks mainly to the low running costs. The 118g/km CO2 figure means annual road tax is a reasonable €160, while later iterations of this engine can achieve up to 4.5 litres/100km - all while producing 141hp. Hatchbacks are more common, but the coupé is sportier and more practical (save for being a strict four-seater). However, they're rare and all body styles drive the same so choose whichever you like the best.
Buy a 1 Series as a practical family car and you'll be disappointed - the Ford Focus or even an Audi A3 does a much better job. However, if practicality is further down your list than efficiency, looks, image and driving dynamics you're along the right lines. The engine range may not be huge, but all versions are talented and economical units, while the rear-wheel drive chassis will please purists.


JEFF the Brotherhood kick off a two-night stand at Kung Fu Necktie tonight. The Nashville band of brothers Jake and Jamin Orrall got the music bug from their father, who has written / produced for Taylor Swift and Reba McEntire. The younger Orralls' brand of music couldn't be much further from that of Swift's though – with a raucous live show and a sound that inevitably finds itself described as grungy, scuzzy and sludgy, JEFF the Brotherhood attract attention through a barrage of loud and distorted but equally quirky and endearing rock songs on their latest Hypnotic Nights LP. Tickets and information for tonight and tomorrow's shows can be found here. Watch them perform "Sixpack" on Late Night with David Letterman below.
XPN welcomes Josh Ritter to The Trocadero tonight. Touring in support of his latest record The Beast in Its Tracks, Ritter has followed up his 2010 full-length So Runs the World Away with an album of deeply personal songs. The time-frame of Beast covers the Americana / folk-rock singer's recent divorce and a period of subsequent rebuiilding, though as NPR's Stephen Thompson points out, the album has uplifting and happy moments to balance the darker ones. Tickets and information for tonight's show with The Felice Brothers can be found here. Watch a live performance of "Joy to You Baby" on Late Night with David Letterman below and listen back to Josh Ritter's World Cafe episode here.
Faun Fables play a show upstairs at Kung Fu Necktie tonight. The project of Dawn McCarthy and Nils Frykdahl, the members of Faun Fables fancy themselves "songtellers," blending the oral tradition of McCarthy's background with experiential music environments like those of gypsies, field recordings and celtic culture. Faun Fables are signed to Drag City and released their most recent effort, Light of a Vaster Dark, in 2010. Tickets and information for tonight's 21+ show can be found here. Watch the duo perform "The Balland of Limpy Glen" below.


The Big Lebowski is The Coen Brothers wild ode to film noir and Hollywood movie conventions. Those with sensitive ears, beware, the language sets a new standard for use of the "F" adjective. But it's believable in terms of the characters. And at the same time it pokes fun at overuse of screen profanity. By the movie's end and a thousand "F"s later, audiences will still be smiling at the amazing resilience of these unique screen characters.
The pure inspiration for The Big Lebowski is a Raymond Chandler detective story. The Dude, the chief Lebowski of this twisted movie vision, is the stand-in for Chandler's detective protagonist Phillip Marlowe, and he does an hilarious job of taking the beatings that the hard-boiled usually endures before solving a case. His adventure begins with physical debasement and ends in a triumph over the darker forces.
Like The Hudsucker Proxy, the stylized nod to madcap comedy of the thirties and forties, the Coen Brothers continue to find movie inspiration from the movies. Theirs is an individual style, a unique point of view that sees everything from another angle. These guys dare to be different in their filmmaking. Their instincts may not always be perfect, and they can wander dangerously within in a film, like the strange Sam Elliot cowboy character that narrates The Big Lebowski and seems to bear no relation whatsoever tot he story. But why not have a cowpoke with a deep-voice subbing for a fairy godmother in a film that includes some stunning dream sequences inspired by the musicals of Busby Berkeley.
Jeff Bridges continues to deliver performances so integral to the movies he's in that they disappear into the landscape of those films. Bridges' longhaired, pot-hazed, Dude is a bigger than life caricature of the sixties dropout. Bridges finds a way to make a sympathetic portrait, even in this surreal landscape. John Goodman, a veteran of several Coen Brothers movies, is a terrific sidekick for Bridges. He brings a strange combination of danger and humor to the shell-shocked Walter. And Julianne Moore leaves an indelible impression as the somewhat Germanic, positively sado-masochistic daughter of phony financier Jeff Lebowski. There are some great cameos from performers as diverse as David Thewlis and Ben Gazzara. Maybe it will become the in thing for actors to do a Coen Brothers film instead of paying homage to the tired films of Woody Allen.
Roger Deakins moves his camera majestically in delivering another elegant look to a Coen Brothers film. In contrast to the stark and icy landscape of Fargo, The Big Lebowski is a delicious bowl of luscious fruit. Carter Burwell's music continues to embellish Coen images successfully and production designer Rick Heinrichs pumps the film with great visual style.
The anamorphic transfer is very posh with a stunning balance of color from the Deakins camera. The dream segments are simply gorgeous with an assault of contrasting elements crystallizing with amazing depth. Resolution is first rate with little indication of over-enhancement. Only the black and white tile elements of the dance sequence seem to ring with added noise ever so slightly. The music tracks are outstanding and the Dolby Digital 5:1 surround is clean, with plenty of air around the diverse elements.
Included in the package is a friendly short on the making of The Big Lebowski primarily featuring the brothers Coen, with smaller segments of Jeff Bridges and John Goodman. Ethan and Joel Coen explain the origins of the film, laugh at themselves and journalists alike, and seem to have a good old time doing what they love to do, making movies.


Microscope World has recently added a variety of microscope reticles to its already wide selection. A microscope eyepiece reticle (also known as a reticule) is placed in the microscope in order to perform measurements or count particles.
The most commonly used microscope reticle is the ruler reticle. Available in a variety of lengths, both mm and inches, the ruler is simple to use and allows the end user to measure the distance between two items in a microscopic sample.
Grain sizing reticles are used for measuring twine, austenite, carbide, brass, and copper. Additionally, grain sizing reticles that meet ASTM E112 grain counting standards, Methods ASTM E-45 A, D and E method, and ASTM C method are specific reticles used to meet government required measuring standards.
Comparator reticles come in a variety of sizes and are sometimes referred to as protractor measuring reticles. Some comparator reticles have different size circles on them as well, helpful in measuring circular particles.
Whipple reticles are grids with a smaller grid inside the large one. The whipple measuring reticle is intended to enable the measurement of smaller specimen features such as pigment dispersions, colloidal particles, dust, and bacteria.
Cross line reticles are available with a simple cross, or more cross lines creating a pie. These cross line reticles are excellent for dividing a specimen into quadrants or measuring angles.
Point array reticles and point dot array reticles are helpful in counting and recognizing patterns.
In addition to the reticles shown above, there are a number of other specialized reticles available including Birnell Scale, Howard Mold counting, Walton & Beckett among many others. You can view all the microscope reticles and stage micrometers offered here.


I am experiencing a time of as-good-as-it-gets with the symptoms of schizophrenia. I always have symptoms, but my symptoms are not always debilitating. Right now, my life looks similar to the lives of most people I know.
During the good times, I try to consciously remember those who have the same illness as me, but who have a much harder time managing their mental illness. Maybe they are without treatment, maybe they are medication resistant, or maybe they lack a network of people to assist them in getting care.
For whatever reason, there are people with schizophrenia who live on the streets and I make an effort not to forget them and not to distance myself from their experiences, because I know there is very little that separates me from them. I could easily be walking in their worn out, secondhand shoes.
That is why I want to thank the people who work at soup kitchens. I want to thank the people that prepare and serve food to the most vulnerable of the mentally ill among us – the people living on the street. I want to thank those people that show up in June, July, September, and not just during the season where everyone is focused on giving – I want to thank the people that are there year-round, serving a hot cup of coffee, asking people how they are doing, and making sure they have nourishing food to give people to eat.
Those people who care for people living in the streets with schizophrenia in the most basic of all ways, by providing them with food, are my heroes. They are the on the front line of mental health treatment and care and they often get overlooked. A person with schizophrenia who is living on the street may not have the insight to seek medical attention, but they will seek out food, and it is in soup kitchens all across the country that this first line of care is taking place.
Most people who cook and serve food in soup kitchens are not social workers, they aren't mental health professionals, they aren't psychologists or psychiatrists, but they are providing life-saving care to those in need.
This month, I was asked if there was anyone who had shown kindness regarding my mental illness. For every meal provided to someone with schizophrenia, I consider that a kindness shown to me, because I have been close to needing those services. It is just luck and circumstance that keep me from the hunger those on the streets have. Some of that hunger isn't just for food, but to know there is someone somewhere that cares.
Soup kitchens are a life line and I would like to thank everyone who works or volunteers at one for being on the front line treating those with a severe mental illness. There are so many ways to offer care – thank you for filling such a critical need.


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Fresh horseradish is absolutely delicious and the good news is it's easy to grow your own. Horseradish is said to have many health benefits and also contains oil called isothiocyanate that has antibacterial and antifungal qualities. This makes me think that companion plants for horseradish might get a huge benefit. Read on to find out about companion planting with horseradish and what grows well with horseradish.
Companion planting is a method of planting two or more plants that have a symbiotic relationship; that is, they mutually benefit one another is some way. Most plants benefit from this practice and planting companions for horseradish is no exception.
As mentioned, horseradish contains oil that helps to curb fungal and microbial movement. While it doesn't prevent infection, it can curtail it which makes horseradish a boon to many other plants, but what are companions for horseradish?
What Grows Well with Horseradish?
If you decide to try this particular horseradish plant companion combo, be advised that horseradish spreads rapidly and is easily propagated from even the tiniest bit of root left in the ground. So plant it at the corners of the potato patch or, better yet, in pots near the patch.
Fruit trees and brambles also make good horseradish plant companions; plant horseradish at the base of small fruit trees or in amongst berries or grapes to get the benefits of its insect repellent qualities. The root is also said to deter birds and small rodents, like moles and field mice, from eating up all the fruit. The same cannot be said of squirrels, however, but (in my experience) nothing deters a determined squirrel.
Sweet potatoes, strawberries, asparagus and rhubarb are all said to make wonderful horseradish plant companions. Again, in every instance, the companion plants seem to get all the benefits from the horseradish.
That's okay, plant it anyway. Horseradish has numerous health benefits for humans too. It is a cancer fighting cruciferous, high in vitamin C, can be used as a gastric stimulant or a topical pain reliever, and don't forget its antibacterial and antibiotic qualities. A very useful plant, one that is easily grown, is companion plant friendly or can stand alone.
Read more articles about Horseradish.


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Hincapie admits doping until 2006, says he agreed to give evidence in Armstrong investigation
by Shane Stokes at 12:04 PM EST comments
Texan's right hand man speaks about 'widespread use of performance enhancing drugs by cyclists'
George Hincapie, the only rider to have ridden all seven of the Tours where Lance Armstrong reached Paris in yellow, has today admitted to doping during that period of his career and also said that he provided testimony in relation to the doping of others.
"Because of my love for the sport, the contributions I feel I have made to it, and the amount the sport of cycling has given to me over the years, it is extremely difficult today to acknowledge that during a part of my career I used banned substances," he said in a statement released at the same time as one by USADA CEO Travis Tygart, which announced his cooperation.
"Early in my professional career, it became clear to me that, given the widespread use of performance enhancing drugs by cyclists at the top of the profession, it was not possible to compete at the highest level without them. I deeply regret that choice and sincerely apologize to my family, teammates and fans."
Hincapie was spoken of as a possible winner of the Tour de France in the year after Armstrong retired, 2006, but according to rumours at the time, he ended up riding that Tour clean due to the fallout from Operacion Puerto. Having finished fourteenth overall and won a stage in 2005 despite riding for Armstrong, he finished back in 31st and never contended again.
His performances also became quieter in the years since, with Hincapie stating today that he rode clean from that point onwards.
"I have competed clean and have not used any performance enhancing drugs or processes for the past six years," he said. "Since 2006, I have been working hard within the sport of cycling to rid it of banned substances. During this time, I continued to successfully compete at the highest level of cycling while mentoring young professional riders on the right choices to make to ensure that the culture of cycling had changed."
Hincapie was regarded as the rider most close to Armstrong and consequently many have stated that his testimony would be the most compelling of all, in terms of plausibility. He has joined up with ten other former US Postal Service riders, namely Frankie Andreu, Michael Barry, Tom Danielson, Tyler Hamilton, Floyd Landis, Levi Leiphimer, Stephen Swart, Christian Vande Velde, Jonathan Vaughters and David Zabriskie in speaking about systematic doping on the team, with those plus 15 other witnesses aiding the investigation.
Hincapie states that it was difficult to speak about the doping of others, presumably referring to Armstrong and his team-mates.
"Two years ago, I was approached by US Federal investigators, and more recently by USADA, and asked to tell of my personal experience in these matters," he said. "I would have been much more comfortable talking only about myself, but understood that I was obligated to tell the truth about everything I knew. So that is what I did."
He retired from the sport this year, ending his time with the BMC Racing Team after the USA Pro Cycling Challenge.
USADA has sent its reasoned decision today to the UCI and to WADA, with Tygart saying that over 1000 pages of evidence has been built in the case against Armstrong. More details of that evidence is expected to emerge later today, and will be carried on VeloNation.com.
Hincapie's full statement is as follows:
For over 30 years I have dedicated my life to cycling. I have always been determined to compete at the highest level, in one of the most physically demanding sports. With hard work and success have come great blessings from the sport I love.
Teammates have become dear friends and I have worked hard to earn the respect of my competitors. I have been associated with managers and team officials whose professionalism is unparalleled. Wonderful fans have supported my family and me since I began this great journey. For all of this and more, I am truly grateful and proud.
Because of my love for the sport, the contributions I feel I have made to it, and the amount the sport of cycling has given to me over the years, it is extremely difficult today to acknowledge that during a part of my career I used banned substances. Early in my professional career, it became clear to me that, given the widespread use of performance enhancing drugs by cyclists at the top of the profession, it was not possible to compete at the highest level without them. I deeply regret that choice and sincerely apologize to my family, teammates and fans.
Quietly, and in the way I know best, I have been trying to rectify that decision. I have competed clean and have not used any performance enhancing drugs or processes for the past six years. Since 2006, I have been working hard within the sport of cycling to rid it of banned substances. During this time, I continued to successfully compete at the highest level of cycling while mentoring young professional riders on the right choices to make to ensure that the culture of cycling had changed.
Two years ago, I was approached by US Federal investigators, and more recently by USADA, and asked to tell of my personal experience in these matters. I would have been much more comfortable talking only about myself, but understood that I was obligated to tell the truth about everything I knew. So that is what I did.
Cycling has made remarkable gains over the past several years and can serve as a good example for other sports. Thankfully, the use of performance enhancing drugs is no longer embedded in the culture of our sport, and younger riders are not faced with the same choice we had.
I am proud to be part of the cycling community, and believe we continue to make positive changes to our sport. I applaud the extraordinary achievements of my fellow riders on and off the bike. Cycling is an incredible sport that not only requires unbelievable physical ability to ride hundreds of miles a day for many days on end; it also requires a certain type of dedication, ambition and character. I have been fortunate to compete with teammates whose commitment and talent will be hard to match. As a rider I have dedicated a large part of my career to helping those teammates succeed. As I begin the next chapter in my cycling life, I look forward to playing a significant part in developing, encouraging and helping young riders to compete and win with the best in the world.
Multiple national masters' champion LeDuc given two year ban for using cocktail of banned substances
Weisel and Verbruggen deny Armstrong's accusations against them
As clock ticks in relation to Qui Tam case, Armstrong implicates Weisel over team doping
Travis Tygart: "The door is closed" to Lance Armstrong after 18-months of non-cooperation
Armstrong meets Bassons in Paris to apologise, but Andreu insists nothing has changed with him
Lance Armstrong settles out of court with Acceptance Insurance in civil fraud suit
Armstrong/US Postal fraud case looks likely to go ahead, with judge to issue ruling on matter within thirty days

redpajama_set_name: RedPajamaCommonCrawl


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MLOps Community
Machine Learning Monitoring
Metadata Storage and Management
Coffee Sessions #50
Creating MLOps Standards
With the explosion in tools and opinionated frameworks for machine learning, it's very hard to define standards and best practices for MLOps and ML platforms. Based on their building AWS SageMaker and Intuit's ML Platform respectively, Alex Chung and Srivathsan Canchi talk with Demetrios and Vishnu about their experience navigating "tooling sprawl". They discuss their efforts to solve this problem organizationally with Social Good Technologies and technically with mlctl, the control plane for MLOps.
0:00 Demetrios **What's going on, everyone? We are back for another edition of the MLOps Community Coffee Sessions. This is a unique Coffee Session because we have two guests. We are lucky enough to be talking to Alex Chung and Srivathsan Canchi – he'll correct me if I pronounced his name wrong, because everyone knows that I am a master at pronouncing names incorrectly. I haven't quite figured out how to pronounce them correctly. But today, what we're going to be talking about is something that is dear to my heart. It is the standardization within the machine learning space – within the MLOps space. And I'm excited to dive into this. I've talked to Alex quite a few times and we have the social good on the YouTube channel. Actually, we can probably start there in just a minute with that. But we have talked a lot about how we can standardize things within this space, because there are so many tools out there, and there are so many opinions being made. And it's not really clear where we're gonna go from here and how we're going to make it easy for everyone – if that is even possible. So that's what we'll be explaining, or exploring, today. First up, maybe we should just go around and hear about who you guys are? Alex, who are you, man? Sri? Who are you? Alex, I'll let you go first.** 1:35 Alex Thanks, Demetrios. I've always really enjoyed our conversations just because the MLOps community's really become, for lack of better words, the community and the hub and spoke for a lot of the conversations that are happening of new ML engineers into the field, but also for those that are looking for a dialogue in a forum. And in this pandemic, it's just, frankly, quite hard to have those organic Coffee Chats at NeurIPs, or these other conferences where normally we'd have very thought provoking sessions, in terms of trying to understand what the direction of the production automation processes is. So, again, my name is Alex Chung. I used to be a PM on the SageMaker team at AWS. Before that, I ran the computer vision DataOps at Facebook. When I was at AWS, my whole role was to figure out of the SageMaker tooling – what can we sell to larger enterprise customers? If we look at these end-to-end ML platforms like SageMaker, that basically do everything. The reality is – not every customer needs everything. The larger organizations that have a strong cloud practice and IT teams are probably thinking about, how do they blend what they have existing with SageMaker with some of these other tooling vendors? And it was my role to figure out with SageMaker, " "How do we pull out these features and glue it into an enterprise's internal ML stack?" And that's actually how I met Srivathsan. He and I became very good friends and he was one of my customers. 3:07 Srivathsan Hey! My name is Srivathsan. Until recently, I used to run the engineering for Intuit's machine learning platform. That's where I've been a customer of Alex's products and SageMaker. Intuit uses a lot of SageMaker capabilities and a lot of home-grown capabilities for building out its ML lifecycle and my team's job was to make sure that ML at Intuit was going on at full speed and we do not have any roadblocks in the way of data scientists dreaming up wonderful models or ideas and putting them out there in production and testing them out. The platform is used to enable all of the ML lifecycle steps and make them really, really smooth so we can actually take an idea from inception to production in less than a week. 4:22 Demetrios **That is everyone's dream, I think. And we've heard that stat being quoted before. I know it makes a few people in the community jealous, to say the least – if not pissed off. But Alex, what is SGT? I am sure that some of the people that are listening have heard or seen some of the stuff that we've been putting on the YouTube channel about SGT and it would probably be good to let people in on what you're doing. What's that effort around it?** 4:58 Alex So the backstory is: when I was at AWS, I realized very quickly that the biggest challenge of selling SageMaker into Enterprises was that there was no common integration point. Each customer of mine had basically built in their own internal libraries and then those libraries connected with services like AWS, or Kubeflow pipelines from open source. And it was basically one big mess. It was clear to me that we needed to build an industry forum for MLOps specifically – so nothing to do with the models themselves – but really on this productionization process. "How can we either converge some of the schemas or incubate certain efforts so that these different tools for MLOps can come together?" So within SGT, it's a nonprofit organization organized here in the United States – we have a 501(c)(3) status. We really break down our work into three categories. First, it's education: we partner/collaborate with various influencers in the field and we really make sure that there's good content and we share it. We also give a lot of advice and feedback. Second, we focused on having discussions of standardization efforts. Basically, when I say standardization, it's a common link between different parts of the MLOps lifecycle: from model serving to your feature serving. Or from your training systems to taking the artifacts to your model serving systems. Then the last component – and this is where I think we've started investing even more time around is – how do we incubate open source projects that can really tie together MLOps? And we think of this as the next generation of orchestration. Historically, orchestration has been around workflow management, but now we see it as the jobs themselves and how they can get composed together from different tools but all in a coherent experience for the data scientists and ML engineer. 6:58 Vishnu **That's awesome – really admirable effort. Hearing you talk about what SGT is doing, what some of your career experiences have been both you, both you, Alex and Sri, the first question that strikes me, that I think a lot of our listeners are asking is, "Why is standardization desirable?" You know, I'd love to understand from the standpoint of a Platform PM in Alex and from a Head of Engineering in Sri – why is it that we look for standardization across different tools? Maybe we can kick it to Sri to start.** 7:29 Srivathsan Alright. I would like to take from my previous experience and how the cloud journey has been. Sometimes history repeats itself and that's very true in the ML space. A lot of the problems that we're seeing in the ML space are not new. If you take the cloud world, for example, in the early 2010 timeframe, there were many players in the cloud space in the first half of the decade. You had AWS, you had Azure coming up, Google is a big player as well. And then you had a lot of the smaller players, and you had the private clouds with the OpenStack console. It's just very difficult to actually build applications and make them portable across the clouds, be able to build a technical quorum, or a group, who can share experiences and share knowledge. So you saw these pockets of expertise – there'll be experts in AWS, there'll be experts in OpenStack, and so on, and so forth. Then came along Kubernetes, who said, "Hey! We will create this control plane which can operate across any cloud and you will have a consistent way of interacting with the controller." Fast forward to today, you run and build an application on your local laptop and run it in production on any cloud, anywhere – with the same set of commands, the same set of interfaces, and you don't really need to learn something brand new just because you're going from a dev environment to a test environment to a production environment. We are kind of living through the dramatic effects of what that standardization has actually unleashed. We can see that Kubernetes is pretty much everywhere right now in terms of application talent. You see the same evolution happening in the ML space. We see a lot of fragmentation in terms of how tooling is being done and there is an opportunity to unleash even more machine learning creativity, which is even better for the entire world, by democratizing the power of machine learning through the standardization efforts. 10:07 Alex I would add to that and say, what's been even more challenging about MLOps in particular is – it's not just one thing that's MLOps. I think DevOps is, you test and then you deploy onto your production instances for an application. But for MLOps, we've kind of seen a whole lifecycle – I like to call it "the big eight stages". If you look at Azure marketing, they call it "nine steps," in AWS, they call it "four". But it's essentially, you have data collection, you have data processing, you have feature engineering, you have data labeling, you have the model design, and you have the training optimization, and then the deployment and monitoring steps. It's a lot of different _fragmented_ pieces. Arguably speaking, for the data scientists, you're really manipulating the data and then you're explaining how the model should be trained and then from there, you almost want to hand it off, because in a production environment, you're scaling it out and you have all these various interconnects in terms of telemetry – so metadata, experimentation, logging artifacts. All of these, in most cases, are independent tools. You very rarely see tools that can bridge across all these different steps. If you even look at things like Kubeflow, the reality is Kubeflow pipelines is the most popular product and that's just a workflow orchestrator. The experimentation system within Kubeflow isn't even that widely used. If we even go through the MLOps community Slacks or the Kubeflow, you actually see a lot of people talking about integrating MLflow with Kubeflow pipelines, which almost defeats the purpose of what Google intended when they built out these tools. And it's completely representative, like this idea of mix and match, in a larger enterprise for various reasons. The first reason is, if we look at any – let's call it a bank, or retailing company, or services company that's aiming to be more tech-centric. When we look at their ML teams, it's not one team, i.e. Lyft is a great example. I think they came and spoke a few months back. Lyft has the ride sharing group and for a long time, they had their own autonomous team. And even within the ride sharing group, there's like three different data science orgs – many of them have different patterns and tooling approaches. And for a platform owner, their focus is, "How do we enable flexibility to do and pick what are the best tools for a use case, while still having that centralized, common-enough experience, and also, more importantly, its governance?" It's also thinking about the ability of tracking all your jobs from spend management and resource allocation that ends up being a lot more of a corporate and decision making process and not so much the traditional "in the weeds of a data science and model". Standards bring all these fragmented pieces together. 13:01 Vishnu **That makes a ton of sense. And I think it's very interesting to hear the perspective from both sides – with sort of the engineering side really being that, there's all this opportunity to simplify repetitive tasks and enable more time to be spent on actual use cases. Really, from a platform perspective, standardization can allow different organizations to more centrally enable machine learning in different ways, without the diffuseness that happens in a lot of different organizations. It looks like Demetrios has a question, so I want to kick it to him.** 13:38 Demetrios **Yeah, I love, how you're saying Sri, the fact that you don't have to know different things for each environment that you're working in when you have that standardization. I know we've seen this a bunch in the community – people talking about how they _do_ need to translate a model, or take it out of a Jupyter Notebook, or do something where they have extra work added on top just because of the process that they've created. I can't remember who (I think it was Hendrik from Tide) I interviewed months ago and he was talking about how he had to change something from R into C, or C Sharp or something. I'm getting this horribly wrong, I'll have to go back and watch it. But they ended up doing some compiler or some translator, like F Sharp that it was, and he just talked about the headaches that that caused. It's really like what you all are trying to get at – to get rid of that, so you can spend less time worrying about that kind of stuff. And then really spend the time doing what you were talking about and enabling the creativity to really blossom and you can see more and more come out of it. So maybe, Sri, can we touch on what's going on at Intuit? How is it… like, how big is the team? How many data scientists are there? I know it's a gigantic team. They're blazing all kinds of new pathways. I see their blog and I love to read what you all are up to. So let's just get the profile breakdown of what it looks like.** 15:38 Srivathsan Sure thing. At Intuit, the data science team is roughly around 250-300 strong with data scientists and around 100-150 ML engineers who support the data scientists in building the models. There are over 400 models in production and most of them were built in the last two, two and a half years. When I joined the team, it was 30… I think 28 models in production – roughly 30. And it just kind of exploded to 400+. We also built a feature store. The feature store, I think, was similar to that of the feature store team that was on your podcast a few weeks back. There are over 10,000 features now, I lost track of how many features there are – and most of these are getting shared across the company from a feature store perspective. And we built a platform that orchestrates between data exploration, model training and then feature engineering, followed by model training and evaluation of the model, followed by productionizing the model, and planning it in prod either in an online context, where it's directly by Intuit's applications, or in an offline context, like batch production space. 17:17 Demetrios **That's incredible to see. The one thing that I think about when we're talking about these standards, and we really wanted to focus on the standards within the enterprise – this is something, Alex, I think you were mentioning before we hit 'record' – is the importance of it and like… Why is it more interesting…? Or do I have this wrong? Is it more interesting for the enterprise to have this as opposed to just those 1-2 men lone wolf teams, (or woman, 1-2 women lone wolf teams)? Like, why is it an enterprise thing?** 17:59 Alex The reason why I think – in larger organizations, they really feel this pain. It hints back at the idea that I shared earlier, that one company is actually composed of many smaller data science teams. And when these different data science teams have different tooling approaches, it's what many people have called a "tooling sprawl". I think it's probably more common in the Kubernetes and classic application stack. But you essentially have five different ways of doing, one telemetry and logging for applications. In MLOps, I think we kind of hit that same spot. There's now many different models servers out there and there's different ways of encapsulating your metadata, whether it's Weights & Biases or MLflow or, you can pick and choose one of the more common Cloud ML platform solutions. Within the context of a large organization, internal standards aim to provide a common interface so you can abstract that boilerplate. I can tell you with very high confidence that the way that SageMaker constructs its training environment, and the way that you would have it in Kubernetes, is almost night and day. SageMaker is going to hotload all your data for you – it's gonna have very opinionated ways of passing in your parameters for your model training. Versus in Kubernetes, it's almost wild, wild west. It lets you, as the application administrator, figure out how the container gets run. But going back to the data scientist who is responsible for the model code, I don't think they really care. At most, they would like to just learn one, it would be even better if they'd have to learn at all. But this idea that, as organizations improve their architecture and production systems, i.e. moving from Airflow, because that's what mostly use for DataOps – and as they get your ModelOps practice, you're adding in Kubeflow pipelines. For the data scientist who has created a model that is now running in production, they're going to need to go ask the ML engineer to rewire many parts of that application to now work for Kubeflow. Or if they're moving to SageMaker, or even have some SageMaker already in other parts of the organization have Kubernetes, this whole "tooling sprawl" means that you have to learn a lot of the individual paginations. I think these new ML orchestrator libraries, these internal standards have emerged within larger organizations that have hundreds of data scientists, like in Intuit's case, that Srivathsan leads. These internal standards really provide that abstraction layer that reduces the barriers to entry for a model in prod. And if we can continue to innovate in that, or even get to a spot where – in open source, we're developing these for many use cases across a number of large customers that are trying to scale out their models in production. I think there's gonna be a lot of value and I think that's where SGT is playing a role right now. 21:03 Demetrios **So if I'm hearing you correctly, what you're saying is that we have the tooling sprawl in Kubernetes world, and there's many different ways to do things, _but_ they all have that underlying compatibility, I guess you could say. But then when you move over to the MLOps tooling sprawl, everything is doing it in their own way. So because there are so many opinions, and so many different routes that you can take, that's where you're losing time?** 21:40 Alex It's where the data scientists have a significant extra amount of work that's irrelevant to the actual model code itself. So once I have that model – either a decision tree model or a deep learning model, although don't think deep learning is as widely used in enterprises just yet – the extra layers of binding that model, in terms of figuring out where the data inputs are, making sure that logs correctly to your metadata service, being sure is picking up the hyperparameters from your optimizer, and then from there, storing that artifact. That's all what I would consider "boilerplate". The data scientist hopefully just learns it once, but in reality, as organizations improve their infrastructure and tooling, they're relearning this over and over again. But if we look at, in the Kubernetes world, there's tools like Helm charts, and there's other very canonical provisioning mechanisms that the SREs and the platform teams then provide to their application engineers. In our landscape, that I've seen at enterprises, they're already working on these different types of systems. Our effort is, "How can we bring this out and make this commonly used?" I think startups and smaller teams are likely to pick up on this once it's been flushed out. But I really think that the biggest users are teams like Srivathsan's, where they are already leveraging a platform like SageMaker, and also have X,Y, and Z that he can talk about as well. 23:11 Srivathsan One thing to add in addition to what Alex said – when I joined the ML team, it used to take about… let's say, it took data scientists a couple of weeks to build the model and the actual model code. It used to take them anywhere between four to six weeks to actually see it in production for all the reasons that Alex mentioned. Even though the platform was running on SageMaker, there were a lot of these things that the data scientist had to learn and tie things together. And that's where large enterprises always face challenges in bringing their ML ideas to production. 23:51 Vishnu **Yeah, that makes perfect sense. I think the way that you guys are describing the narrative here resonates a lot with what previous conversations of ours at different organizations, and with different technical leaders, have stated. I think I'm curious now to maybe get into a little bit more of the technical side, which is something our listeners are always asking for and wondering about. So from an organizational standpoint, understanding these toolings, this question of tooling sprawl, this notion of wanting to move towards time to production – how do you see, technically, the standards evolving? How should engineers maybe make use of the work that you guys are doing in terms of standardizing the different interfaces between MLOps tools?** 24:41 Alex That's a loaded question and there's a lot of different ways that we can take this first. I think our core audience today – and this will expand over time – is the teams that are building ML systems and servicing, let's call it, greater than 50 data scientists. At that point, you're not usually just using one tool, and you have a variety of different systems and mechanisms. So between the different organizations, primarily enterprise customers, and also a number of startups, and just interested MLOps enthusiasts in the field – we're working on both conversations from "how we can converge different implementations?" in terms of "How do you start a model server?" Or "How do you bind your model artifact to all the different other layers of your MLOps tooling kit (the metadata service, the artifacts store, etc.) in a common experience?" But we're also actually trying to build out some of these libraries. So Srivathsan and I have been collaborating on a project that's called mlctl – in a second, I can let him talk about that further. But the ML platform teams that are actually facing this reach out to us and we'd love to have a conversation with you to bring out what your needs are and where we see the common overlaps. In almost every company, if you're greater than, let's call it 25 data scientists, we've seen very consistent problems that are not related to the underlying tooling stack – while I'm sure those still exists – but it's "How do you compose all these different tools together from a common experience?" And across the board, we haven't really seen much innovation in _this_ part of the MLOps training yet. So that's the most tangible piece that ML platform engineers and individuals working in larger enterprises can get from this conversation. Come participate with us, we have a number of different discussions and a very tangible way of how we're trying to solve this with one of these open source libraries that we're working on incubating and pulling out. Srivathsan, do you want to just chat about mlctl? I know it's still fairly early, but we do have a lot of interesting ideas and demos out there, coming out as well. 27:02 Srivathsan Absolutely. Mlctl is our take on how to approach this tooling sprawl and provide a common interface to our data scientists. Mlctl actually started off as an internal project and is widely used at Intuit by our data scientists to go between the various stages, like model training happens on SageMaker, model batch hosting happens on the Kubernetes model, online hosting happens on SageMaker, where they have a common interface to do all of these lifecycle stages. So what we're working on in the open source is a stripped-down version of the mlctl library, but with adding a lot more capabilities for interacting with platforms like Azure, Kubernetes, and SageMaker, and providing the capabilities so these can interact. There's a lot of interesting things that we are adding to the mlctl ecosystem, including things like, "Hey, can I get started from scratch on an ML model? And can you provide me with an opinionated way to do that?" So we build capability for data scientists, or anyone, to just come in and say, "I want to get started on ML." Mlctl provides a way to do that. Alex mentioned the word boilerplate – we actually allow for fairly simple boilerplate that can then be imported into a notebook and then starting to innovate on top of that. And we'll also be adding support for the workflow orchestration where there are various pieces and we are brainstorming different ideas of how we would do that. There's also the notion of "How do you publish experimentation metrics?" And as your model training is happening, "How do you publish those to the MLflow [inaudible]?" Also we are also building as part of the ecosystem, libraries that allow for consistent ways of transferring this information, persisting the metadata, persisting the artifacts, into their respective systems, and mediating some of that work. Go ahead, Alex. 29:53 Alex Let me summarize this and explain what got me so excited about this mlctl project that Intuit has been working on. As a previous person and vendor – so I'm not AWS anymore, in case that wasn't clear earlier – but when I was trying to figure out how we take the AWS APIs and make it so that an enterprise's data scientists can access it. I always have to go through this middleman and that is the ML platform team. They act almost like the "gate" – the protection for the data scientists from all the vendors that are trying to annoy them and get them to use their products. And that's okay, you know? I've come to really appreciate how important they are in terms of centralizing the common efforts. What mlctl provides for those data scientists is a common interface, a one SDK, that can basically route the individual jobs. And when I say "jobs," I mean a processing job, a model training job, a model deployment job, a batch inference job. And then, the ML engineers can compose it in a KFP, Kubeflow pipeline', or an Airflow pipeline's DAG, that actually runs all those jobs together. It's this idea that the jobs themselves should be portable, because in a multi cloud setting, that's actually very valid. 76% of compute is still on premise and this was out of a Morgan Stanley survey that came out, I believe in Q2 of this year. And within MLOps, there's just so much to learn. The boilerplate really abstracts a lot of that complexity. And for any startup or any open source tool that's trying to get into an enterprise, they should think about, "What are the different gates that can obstruct it?" Because otherwise, if an open source tool has to go to market with five different data science teams just to really see that they have all of the data science in a large enterprise, that's just a significant amount of effort. It's these common SDKs that has to emerge to simplify that handoff process. 31:54 Vishnu **That's a really impressive concept. This idea almost of a – I don't know if I'd call it a "master library," but it sounds like that – that sits between where data scientists are trying to do machine learning and where platform teams are almost trying to control machine learning or getting get a grasp on all the different demands that can come from a decentralized (often) function like data science, where each of scientist themselves can be a consumer. Is it correct, Sri, that this project came out of Intuit as an open source project?** 32:32 Srivathsan Yes. Intuit Open Source, mlctl, and a few other tools in that ecosystem – we call it a tool called Baklava. In the open community, we built another tool called Sriracha. Alex was one of the primary drivers of that. So we've already started to see how the ecosystem can be built and open, and how we can make more value. 33:02 Demetrios **I've just got to give props to whoever named the tool Baklava after I spent the weekend in Greece, eating a lot of Baklava. I thank you for whoever that was that named it and I'll kick it back over to Vishnu.** 33:17 Vishnu So [laughs] that's making me hungry – Baklava, Sriracha, mlctl. Okay, and all of these… 33:24 Alex We've got a food thing going on. Omakase – that's coming down the line. A preset menu. 33:30 Vishnu **[laughs] You need one called Pizza to confuse people. Okay, so all of these projects were open sourced out of Intuit. And I'd love to understand, particularly with mlctl, where – I think Sri, you alluded to this with certain demos and the ability to import a model and work with it – where is mlctl? Where are these projects today? And where do you see them going tomorrow? If community members in the MLOps community or elsewhere want to get involved, how can they do that? Where would be the best places to get started with each of these projects?** 34:05 Srivathsan Absolutely. So, mlctl is on GitHub and I can share the link with you. We have a Slack channel – that way the community members can come in and start participating. We have a roadmap of enabling quite a few use cases around Azure and KFP pipelines and so on. And we _need_ community participation. We really want to have more people from the community coming in and helping us build that pipeline. It's a very small team of people who are very passionate about this. So we're kind of building it out, but I would really love to have a community around this. So, talk to Alex or me and we can get you started on it. 34:58 Alex I would say if you can't find our emails just add us on LinkedIn, we're very open to having a lot of these MLOp- related conversations. But let me just take it from my own lens – from a lot of our late night conversations around the world of MLOps. There has to be the standardization framework at some point, whether we call it a "standard" or an "opinionated framework," I'm open to suggestions as the dialogues unfold. But the piece around gluing different systems together that are fragmented, it just makes too much sense. It just _has_ to happen – at least an effort around that – for a good period of time. Because otherwise, there's just too many tools to manage for any one individual. I think if we were to say six months from now, "Where should this be?" I think it would be premier support for two commonly used ML platforms. So I think right now we're triangulating on SageMaker, Azure ML, Kubeflow pipelines, and then also Databricks. I think those are probably the four that we hear the most across the board, time and time again, "We'd like support for the X, Y, Z." Surprisingly, GCP hasn't really popped up as high as we thought it might. But between those four, I think that's really where we see a sweet spot right now. For the end user, in that six month time period, you should be able to define your job and you can swap out Databricks with SageMaker, or Kubeflow pipelines for orchestration with another tool with minimal effort – i.e., your code doesn't change, it should just be config settings that change. That would be the target state. And I think in a year's time, we'd really like to see enterprises that have resource scale, once again, that 25-50+ data scientists that have multiple tools within their MLOps ecosystem internally – they can use a standardization library. I know having talked to probably 25+ large enterprises, that they all have some flavor of this. Some of it is minimal capabilities, while others are actually significant, to the point of a mlctl-like feature system. I don't think there's that much value for these teams to build it all themselves because the core tenants are all the same in terms of how we're thinking about this: 1)portability of the underlying infrastructure, 2) great developer experience, really providing abstraction for the data scientist developer, and then 3), I think it's finally this idea of flexibility in terms of how you compose these tools together. The best way to get involved, if that vision resonates with any members of this MLOps community, is to reach out to us. We'd love to talk more about where there's overlap and really see if we can create some common tasks in terms of knocking out use cases that make sense for any of the community members – in their settings – and also in the larger enterprises that we're actively taking this to market with. 37:56 Vishnu **Very, very, very cool, guys. Thank you so much for sharing that about mlctl. I think one thing that really stands out to me – that you guys have reiterated throughout this conversation – is that how we work in ML just has to be… it has to kind of coalesce, right? That process element, that boilerplate element – it's got to look more similar. It _will_ look more similar. Going back to what Sri said about how cloud infrastructure ultimately started to have a similar workflow. And I think the idea that mlctl can be one of those tools that helps with that level of coalescing is really great. I think if there's one more question that I have to kick it to Sri, it's "Can you tell us an example of how mlctl helped your workflow at Intuit or how it's in production at Intuit?" So that other companies or other people that are out there who are interested in adopting it, can understand the value to your organization in the past.** 39:01 Srivathsan I'll take this conversation back to the top, where Alex was alluding to these different groups at Lyft. Similarly, I tend to have different business units and one of them happens to use Airflow, because they love to orchestrate their workflows using Airflow, while the other group loves to use Kubeflow. So now you have these two different technologies that are doing workflow orchestration. However, as a company, we want to make sure that our models are running with a certain level of compliance, and security, and logging, monitoring, and all the good stuff that goes with operationalizing any code. And that's where the platform team comes in and says "Hey! You use the orchestration tool of your choice, but we would like to make sure that these certain basic tenets are followed." So that's where mlctl comes in. Mlctl is deeply integrated into both the Airflow and the Kubeflow pipelines and it provides a consistent interface no matter where you're coming from. In fact, we have a third group, which is using it from Jenkins – just orchestrating stuff from Jenkins – and it all goes through the same rigor that you would go through if you went through a platform provider to approach your tools. I mean, the ML platform itself provides a nice UI and so on to do all of the MLOps stuff and makes it easy. But it also provides a choice by providing this library, which allows you to achieve the same level of productionalization, rigor, and maturity with the tool chain of your choice. 41:01 Demetrios **I'm wondering, Alex, about when you look at the evolution of this, and you laid out your goals, and I'm going back to the Kubernetes world, and I know that they have CIGS in the Kubernetes world. And those CIGS are mainly headed up by different companies who have a stake – more tooling companies, I would say than anything. Do you feel like it's going to adapt into that? Where it's going to be the different MLOps tooling companies who will be leading different CIGS to try and help evolve the different spaces?** 41:38 Alex I can see that definitely being the case. I'd say, what we've seen is a little different in MLOps is – from the perspective of open source, because this is where _all_ this falls under. I think that's an implicit assumption here. There's multiple forms of open source. There's what I would consider – open source is highly tied to a platform – so if you think of the AWS CLI or the Boto3 API – technically speaking, it's open source, but it's directly tied with AWS. There's another version, which is called "exhaust open source," which is, for example, Metaflow or Feast – it came out of an enterprise that had a specific problem that could not be addressed from the existing vendors and tooling out there. They made it for themselves and they pushed it out into open source to give to other users. There's minimal to some degree of support. Airflow is another great example of this at Airbnb. And then there's what I consider "commercial open source" that's like the Prefect of the world, or Seldon Core from the Selden model-serving startup. And I think you'll see a confluence of both vendors and startups that are advocating for a vertical to grow, and as a natural extension, their product within that vertical will definitely play a role. We have different participants like Seldon actually come to our working group meetings and video with some of their open source projects. I think the other side of this equation is projects like mlctl – there's no vendor or startup behind mlctl. Maybe the answer is "yet." But the framing here is, for an enterprise that has to leverage multiple tools, they need to have a solution. And they don't necessarily want to maintain all of it themselves, or add all the features themselves because it's a significant amount of effort and ML platform teams tend to be very small relative to the number of data scientists within an organization. I think it'd be a mix of both. And I think SGT aims to really be a place where we focus on MLOps as a nonprofit forum where we can incubate these ideas and projects, and really host an avenue to dive deep into this space. 43:58 Demetrios **That's so cool. Yeah, I've drank the Kool Aid. [chuckles] I'm fully on board with this vision. I love it. Guys, you know – I mean, we've talked about it a few times and we have the different working groups that are going on right now. We're throwing all of the recordings onto the YouTube channel. So if anyone wants to catch up on what they may have missed, you can go and check those out. And if you want to jump into a working group, they happen once a month on Tuesdays (?).** 44:31 Alex That's right, it's once a month on Tuesdays. It's really an area where different people present ideas and also, now that mlctl has really aimed to be a connection point, I think we'll be seeing a number of different presentations coming into it as well in some of these working groups for the foreseeable future. But really, I think if there's others in the committee that have ideas around this – that want to present and discuss this with the group – we'd love to have conversation and really see if there's an avenue for taking new ideas that aggregate the different pieces of the tooling stack together. That coalescing concept. 45:10 Demetrios **Yes. So cool. Well, thank you both for coming on here. Thank you, Vishnu as always, for being the incredible copilot. Or I think pilot, maybe, and I'll be the copilot. [chuckles]** 45:23 Vishnu **I don't think so [chuckles] Okay. All right. ** 45:25 Demetrios **[laughs] So, this has been awesome. And this is one that I've been wanting to talk to you about for a while now, Alex, ever since you sold me on the vision months ago. It's finally coming to fruition and I'm very happy about it. So we got it done. We did the podcast, and you had mlctl to show for it. So it's good that we waited a little while and were able to talk about that. A great work, Sri, and all of the cool stuff that you all are doing. I am _super_ stoked to see, like, where this goes, and to be the biggest cheerleader. [chuckles] So that's about all the time we've got for today. I think that is it. Unless anyone else has anything else to say, we're going to end it there.** 46:12 Alex I think MLOps is an early field. And there's a lot of different ideas that we're going to see, whether it be folks like us, who are presenting on this podcast, or in very bespoke different parts of the community – all the way down to random startups that literally went from a small seed round like Snorkl, to $1 billion valuation little under two years. Very, very quick, rapid development. And I think the fact that this early community is here means a lot of us are exploring and testing new ideas and theses. The biggest thought that always crosses my mind is really longevity to see the space, which will really develop the wisdom within the members who decide to remain active in the community in terms of "How do we really add value for society with ML?" I think that's ultimately the goal here. It's not just to take models into production, it's to move a business metric, which hopefully results in more efficiency or a whole new experience that can drive what the human condition can do, using technology. And thank you for having us, in terms of letting us show our pitch, and we should definitely continue having these types of conversations. Maybe even do like some type of Coffee Session where Srivathsan's team can show mlctl and its current state in a couple of months. 47:24 Demetrios **Yeah, that would be really cool. That would be very cool. So we will keep that for the next one. So, everyone out here that's listening has to wait patiently. Thanks again, guys. This was awesome.**
Alex Chung
Build MLOps Interoperability, Social Good Tech
Alex is a former Senior Product Manager at AWS Sagemaker and a ML Data Strategy and Ops lead at Facebook. He's passionate on interoperability of MLOps tooling for enterprises as an avenue to accelerate the industry.
Srivathsan Canchi
Head of Engineering , Machine Learning Platform, Intuit
Demetrios Brinkmann
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Vishnu Rachakonda
Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.
©2023 MLOps Community. All rights reserved unless states. Images provided by Unsplash.com and pexels.com. Made with ♥, tea and biscuits.


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Support publishers: Android mainly, Unity, Cordova/Ionic, Adobe Air, etc..


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Not to be confused with Icaridin.
It is thought that icariin is an active component of Epimedium extracts, as it has been shown to share several mechanisms of action with compounds used in Western medicine to treat impotence. In particular, icariin is a weak PDE5 inhibitor in vitro and enhances the production of nitric oxide, as well as mimicking the effects of testosterone in rats. It is a weak antioxidant and has antidepressant-like effects in mice. It also has neuroprotective effects in rats.
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↑ Pan Y, Kong L, Xia X, Zhang W, Xia Z, Jiang F (December 2005). "Antidepressant-like effect of icariin and its possible mechanism in mice". Pharmacol. Biochem. Behav. 82 (4): 686–94. doi:10.1016/j.pbb.2005.11.010. PMID 16380159.
↑ Pan Y, Zhang WY, Xia X, Kong LD (December 2006). "Effects of icariin on hypothalamic-pituitary-adrenal axis action and cytokine levels in stressed Sprague-Dawley rats". Biol. Pharm. Bull. 29 (12): 2399–403. doi:10.1248/bpb.29.2399. PMID 17142971.
↑ Pan Y, Kong LD, Li YC, Xia X, Kung HF, Jiang FX (May 2007). "Icariin from Epimedium brevicornum attenuates chronic mild stress-induced behavioral and neuroendocrinological alterations in male Wistar rats". Pharmacol. Biochem. Behav. 87 (1): 130–40. doi:10.1016/j.pbb.2007.04.009. PMID 17509675.
↑ Luo Y, Nie J, Gong QH, Lu YF, Wu Q, Shi JS (August 2007). "Protective effects of icariin against learning and memory deficits induced by aluminium in rats". Clin. Exp. Pharmacol. Physiol. 34 (8): 792–5. doi:10.1111/j.1440-1681.2007.04647.x. PMID 17600559.
↑ Zheng M, Qu L, Lou Y (May 2008). "Effects of icariin combined with Panax notoginseng saponins on ischemia reperfusion-induced cognitive impairments related with oxidative stress and CA1 of hippocampal neurons in rat". Phytother Res. 22 (5): 597–604. doi:10.1002/ptr.2276. PMID 18398927.