Data science and machine learning are helping to drive business decisions and generate income and insights for organizations across a spectrum of industries, from oil and gas to financial services and many in between. However, developing and deploying ML workflows can be challenging.In this session, we’ll explore how to incorporate data science and AI/ML into Kubernetes development workflows, taking advantage of the platform’s openness and rich ecosystem. Using well-known open-source tools for Data Science such as Jupyter Notebooks and TensorFlow, we will explore different strategies to accelerate and automate ML workloads with Kubernetes.
Senior Principal Developer Advocate at Red Hat