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 ✌️ ------------- 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."