This deck provide an overview of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers. Next, it showcases the key capabilities required in a containers and kubernetes platform to help data scientists easily use technologies like Jupyter Notebooks, ML frameworks, programming languages to innovate faster. Finally it discusses the available platform options (e.g. KubeFlow, Open Data Hub, etc.), and some examples of how data scientists are accelerating their ML initiatives with containers and kubernetes platform.