Reflection – Learning Launch

Challenge

I am a member of my organization’s engineering team and our challenge is to develop a compelling and  robust software engineering solution for our customer. Our customer is an automobile company and the  challenge is to develop a software tool/service that tests the Artificial Intelligence (AI) Software that  makes the car more autonomous. For example, there would be a software to test Tesla’s Autopilot feature  and tell how good/bad it is doing. We are trying to do something similar that is in line with our customer’s  expectations. We are a team of 6 and the allocated time for the project is 6 months. With the learning  launches that take place alongside the incremental software development activity during the 6 month  time period, we will focus on trying to uncover what our customer appreciates and what needs more work  in order to improve a certain functionality offered by the software tool.

Selection

We are going to go ahead with the learning launch tool in order to quickly engage with the customer and  test our key customer assumptions. Learning launch is a fast and inexpensive experiment that allows us  to test assumptions, ideas and prototypes.

Application

Learning Launch 0.1: Prior to engaging with the customer, we developed a prototype of our software tool  and had our ‘Learning Launch 0.1’ event one month into the project. Although all the internal  functionalities of the prototype wasn’t yet ready, the User Interface (UI) of the prototype in fact was quite  advanced, and with this UI we were able to showcase the various offerings of the software tool. After a  comprehensive demonstration of the prototype, we let the customer experiment with the prototype by  navigating through the UI of the prototype on the computer screen. Through this hands-on exercise, the  customer got a look and feel of the prototype (and in a sense what the eventual end product would be  like) and this tested our assumption about how easy it is to use the tool and how the tool provides various  metrics (example: The accuracy of the Artificial Intelligence Software is 81%) that the customer expects.  Broadly speaking, this tested the assumption that our understanding of what the tool should offer is in  line with customer expectations.

Learning Launch 0.2: About 4 months into the project, the first version of our end product (software tool)  was ready. In order to perform more iterations on our end product based on customer feedback, we had  our ‘Learning Launch 0.2’ event. Here, similar to ‘Learning Launch 0.1’ the customer was able to use the  software tool, get a good look and feel of it and also got to play around with some sample data we had  made available. This sample data consisted of various images that one would see while driving on the  road. The software tool would rate the performance of the Artificial Intelligence (AI) Software (example:  Tesla’s autopilot) and provide feedback on how good the AI software is in detecting objects such as  pedestrians, other cars, trucks, traffic signs etc., that were present in the sample image data. This tested  our assumption that the software tool is robust and in fact made use of data varied in both quality and

quantity in order to test the AI software system. We also asked our customer to participate in a survey  along with us. This survey consisted of questions such as which functionalities of the software tool needed  more work?, which functionalities were highly mature?, did the team need any additional resources to  complete the project within the 6-month time period? And any additional comments if any.

Insight

By applying the learning launch tool, our team identified our assumptions and designed launches to test  these assumptions. From the first learning launch, we got feedback on the user experience (UX) of the  software tool and it was pointed out to us that this experience could in fact be improved by not throwing  in a lot of information at the computer screen, but rather allowing the user the option to see advanced  information. From the second learning launch, we learned that speed of the tool (how fast it responds to  the user) could be improved. We also learned that the customer was happy with the variety of data that  was used to test the AI system. Going further, our team will review the mentioned insight.

Approach

The reviewed insight will be incorporated in our next learning launch: ‘Learning Launch 0.3’. This learning  launch is expected to happen before the 6-month time period ends. The incorporation of insight is in  progress and is quite significant as the next launch comes before a full rollout of our end product. ‘Learning  Launch 0.3’ will reflect insight gained, progress made, and come closer to a robust and compelling  software engineering solution for our customer.


Reflection – Learning Launch Challenge I am a member of my organization ’s engineering team and our challenge is to develop a compelling and robust software engineering solution for our customer . Our customer is an automobile company and the challenge is to develop a software tool / service that tests the Artificial Intelligence ( AI ) Software that makes the car more autonomous . For example , there would be a software to test Tesla ’s Autopilot feature and tell how good / bad it is doing . We are trying to do something similar that is in line with our customer ’s expectations . We are a team of 6 and the allocated time for the project is 6 months . With the learning launches that take place alongside the incremental software development activity during the 6 month time period , we will focus on trying to uncover what our customer appreciates and what needs more work in order to improve a certain functionality offered by the software tool . Selection We are going to go ahead with the learning launch tool in order to quickly engage with the customer and test our key customer assumptions . Learning launch is a fast and inexpensive experiment that allows us to test assumptions , ideas and prototypes . Application Learning Launch 0.1 : Prior to engaging with the customer , we developed a prototype of our software tool and had our ‘ Learning Launch 0.1 ’ event one month into the project . Although all the internal functionalities of the prototype was n’t yet ready , the User Interface ( UI ) of the prototype in fact was quite advanced , and with this UI we were able to showcase the various offerings of the software tool . After a comprehensive demonstration of the prototype , we let the customer experiment with the prototype by navigating through the UI of the prototype on the computer screen . Through this hands - on exercise , the customer got a look and feel of the prototype ( and in a sense what the eventual end product would be like ) and this tested our assumption about how easy it is to use the tool and how the tool provides various metrics ( example : The accuracy of the Artificial Intelligence Software is 81 % ) that the customer expects . Broadly speaking , this tested the assumption that our understanding of what the tool should offer is in line with customer expectations . Learning Launch 0.2 : About 4 months into the project , the first version of our end product ( software tool ) was ready . In order to perform more iterations on our end product based on customer feedback , we had our ‘ Learning Launch 0.2 ’ event . Here , similar to ‘ Learning Launch 0.1 ’ the customer was able to use the software tool , get a good look and feel of it and also got to play around with some sample data we had made available . This sample data consisted of various images that one would see while driving on the road . The software tool would rate the performance of the Artificial Intelligence ( AI ) Software ( example : Tesla ’s autopilot ) and provide feedback on how good the AI software is in detecting objects such as pedestrians , other cars , trucks , traffic signs etc . , that were present in the sample image data . This tested our assumption that the software tool is robust and in fact made use of data varied in both quality and quantity in order to test the AI software system . We also asked our customer to participate in a survey along with us . This survey consisted of questions such as which functionalities of the software tool needed more work ? , which functionalities were highly mature ? , did the team need any additional resources to complete the project within the 6 - month time period ? And any additional comments if any . Insight By applying the learning launch tool , our team identified our assumptions and designed launches to test these assumptions . From the first learning launch , we got feedback on the user experience ( UX ) of the software tool and it was pointed out to us that this experience could in fact be improved by not throwing in a lot of information at the computer screen , but rather allowing the user the option to see advanced information . From the second learning launch , we learned that speed of the tool ( how fast it responds to the user ) could be improved . We also learned that the customer was happy with the variety of data that was used to test the AI system . Going further , our team will review the mentioned insight . Approach The reviewed insight will be incorporated in our next learning launch : ‘ Learning Launch 0.3 ’ . This learning launch is expected to happen before the 6 - month time period ends . The incorporation of insight is in progress and is quite significant as the next launch comes before a full rollout of our end product . ‘ Learning Launch 0.3 ’ will reflect insight gained , progress made , and come closer to a robust and compelling software engineering solution for our customer .