Declarative MLOps - Streamlining Model Serving on Kubernetes

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 practices for containerizing your own model, and CI/CD using GitOps.