Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools
Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools


Data scientists desire a self-service, cloud-like experience to access ML modeling tools, data, & compute resources to rapidly build, scale, reproduce, & share ML modeling results with peers & software developers.

Kubernetes & container platforms provide desired agility, flexibility, scalability, & portability for data scientists to train, test, & deploy ML models quickly, without IT dependency.

The session will 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, we will review 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 we will share the available platform options (e.g. Red Hat OpenShift, KubeFlow, etc.), and some examples of how data scientists are accelerating their ML initiatives with containers and kubernetes platform.

Key Takeaways

1. Containers and kubernetes platforms accelerate ML workflows, and streamline collaboration with software developers
2. Options exist to consume ML tools powered by containers and kubernetes
3. Best practices and gotchas around operationalizing containers and kubernetes for ML workflows based on real world deployments


Tushar Katarki is a senior technology professional with experience in cloud architecture, product management and engineering. He is currently at Red Hat as a product manager for OpenShift and the lead for Machine Learning on OpenShift. Prior to the current role, Tushar has been a product manager and a developer at Red Hat, Oracle (Sun Microsystems), Polycom, Sycamore Networks and Percona. Tushar has a MBA from Babson College and MS in Computer Science from University at Buffalo.

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