//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 ✌️ -------------

Join our slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

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 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."