Declarative MLOps - Streamlining Model Serving on Kubernetes // Rahul Parundekar// MLOps Meetup #123

MLOps Community Meetup #123! Last Wednesday, we talked to Rahul Parundekar, Founder of A.I. Hero, Inc.

//Abstract
Data Scientists prefer Jupyter Notebooks to experiment and train ML models. Serving these models in production can benefit from a more streamlined approach that can guarantee a repeatable, scalable, and high velocity. Kubernetes provides such an environment. And while third-party solutions for serving models make it easier, this talk demystifies how native K8s operators can be used to deploy models along with best …

abstract benefit best practices community data data scientists environment experiment founder jupyter jupyter notebooks k8s kubernetes meetup ml models mlops notebooks operators practices production scalable scientists solutions talk