Coffee Sessions #40 with Srivatsan Srinivasan of AIEngineering, Scaling AI in Production.  

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// Abstract
This Coffee Session is a Collaboration with @AIEngineering. Srivatsan Srinivasan, the founder of the popular AI Engineering channel on YouTube and a senior partner at a major technology consultancy. Sri has a true passion for building ML systems, and his channel engages in some of the most detailed, thorough, and complete treatments of the MLOps topics we often discuss in the community. It's no surprise, then, that his coffee session with us went similarly!

This conversation became pretty technical. We delved into details of how to set up CI/CD, how to set up the right kinds of tests, which cloud tools stand out to us on GCP, AWS, etc., and many other topics. Sri had a wealth of knowledge on all these fronts!

// Bio
20+ years of intense passion for building data-driven applications and products for top financial customers. Srivatsan has been a trusted advisor to a senior-level executive from business and technology, helping them with complex transformations in the data and analytics space. Srivatsan also runs a YouTube Channel (AIEngineering) where he talks about data, AI, and MLOps.
// Related Links
AI and MLOps free courses - https://github.com/srivatsan88
YouTube channel: bit.ly/AIEngineering

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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Sign up for the next meetup: https://go.mlops.community/register
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Srivatsan on LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/

Timestamps:
[00:00] Introduction to Srivatsan Srinivasan
[01:41] Background on YouTube AIEngineering
[03:17] Tips on learning MLOps and starting with the field
[06:00] "Focus on your key challenges, and that will drive the capability that you need to implement."
[06:50] Tips on starting CI/CD
[08:46] "Start with DevOps and see what additional capabilities you will require for the Machine Learning aspect of it."
[09:24] Staying general in different environments
[10:43] "Focus on the core concepts of it. The concepts are similar."   
[12:10] Testing systems robustly
[20:00] Trends within MLOps space
[20:31] "Everybody can fail fast, but you need to fail smart because Machine Learning is a huge investment."
[23:21] GCP Auto ML
[26:54] Deployment
[27:06] "It's not only the tools, but it's also the patterns."
[29:34] Kubernetes perspective
[31:21] Favorite model release strategy
[36:22] Annotation, labeling, and concept of ground truth
[38:10] Best practices in Architecture and systems design in the context of ML
[41:29] "You learn a lot, at the same time the complexity also increases, so work with multiple teams in this process to learn it."  
[42:35] "Your speed increases based on the way you envision your architecture."
[42:55] Software engineering lifecycle vs machine learning development life cycle
[44:55] Youtube experience
[45:50] "My focus has always been from intermediate to experts."
[46:24] Content creation
[47:17] "You cannot do everything in MLOps at one stretch. You have to see what is critical for you."
[47:23] "For me, continuous training is not that critical because I don't want to take the freedom out of the data scientists."
[48:31] New contents planned
[48:40] IoT and Edge Analytics - Predictive maintenance  
[50:21] Wrap up