Come, join Abhinav Joshi and Tushar Katarki from Red Hat, at ODSC East (April 13-17th in Boston, USA) to learn how Kubernetes and containerized data science tools can help accelerate the ML lifecycle.

Organizations are striving to serve their customers better, gain competitive advantage, increase revenue, save costs, and be more secure. Being able to roll out and frequently update AI/ML-powered intelligent software applications plays a critical role in helping organizations achieve these business goals. This challenges data engineers, data scientists, Machine Learning (ML) engineers, software developers, IT operations, etc. to collaborate extensively, and find ways to accelerate the ML lifecycle and application development (e.g. data ingestion and preparation, ML modeling, application development, inferencing, model monitoring, and management).

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. Software developers want to streamline the process of integrating the ML models into application development and deployment processes. And, both these personas don’t want to depend on IT operations for every infrastructure and platform resource request. 

Kubernetes & containers powered Hybrid/multi-cloud platforms with integrated ML and DevOps tools and frameworks can provide the capabilities that data scientists and software developers need to securely build, and deploy AI/ML-powered intelligent software applications in a consistent way across data center, public clouds, and edge. 

This breakout session at ODSC East will provide an overview of containers and Kubernetes, and how these technologies are helping organizations solve the challenges faced by data scientists, ML engineers, and software developers. Next, we will review the key capabilities required in containers and Kubernetes powered hybrid/multi-cloud platform to help data scientists easily use technologies like Jupyter Notebooks, Python, TensorFlow, etc. to accelerate ML lifecycle. Finally, we will share the available Hybrid/multi-cloud platform options (e.g. Red Hat OpenShift, KubeFlow, Open Data Hub, etc.), and some real-world examples of how data scientists and software developers are accelerating the delivery of AI/ML-powered intelligent applications with containers and Kubernetes.

We look forward to seeing you at ODSC East in Boston very soon!