Scaling AI in production // Srivatsan Srinivasan // MLOps Coffee Sessions #40

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

//Abstract

//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 transformation in the data and analytics space. Srivatsan also run a YouTube Channel (AIEngineering) where he talks about data, AI and MLOps.

//Takeaways
Understand the role and need of MLOps
Prioritize MLOps capability
Model deployment
Importance of K8s

//Other Links
AI and MLOps free courses - https://github.com/srivatsan88
Youtube channel: bit.ly/AIEngineering

--------------- ✌️Connect With Us ✌️ -------------
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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 start with the field
[06:00] "Focus on your key challenges and that will drive your 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] "It's a two-way process. I learn then I teach."