Abstract: 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.
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
Bio: Abhinav Joshi is a Sr. Principal Marketing Manager at Red Hat, focused on AI/ML workloads on Kubernetes and Containers powered Red Hat OpenShift Hybrid Cloud Platform. Abhinav has over 19 years of broad industry experience around Hybrid Cloud, Big Data Analytics, Data Management, and Digital Workspace focused products and solutions. Throughout his career, Abhinav has held strategic roles in Product Management, Marketing, Sales, and Consulting Services. He holds MS in Systems Engineering from the University of Maryland College Park, Graduate Certificate in Strategic Management from Harvard University, and a BS in Chemical Engineering from Nagpur University, India.