In the world of MLOps, where data science and machine learning meet operational excellence, how Kubernetes, the powerful container orchestrator plays a crucial role in building scalable and resilient ML production pipelines. Attendees will dive into the intricacies of leveraging Kubernetes for MLOps. The session starts by addressing common questions about K8s cluster design for MLOps. Then we explore how Kubernetes acts as a catalyst in the evolution of MLOps. Discuss Kubeflow, an open-source project, that simplifies ML workflows on K8s, offering scalability and portability. This talk explores major Kubeflow components and demonstrates their usage in solving MLOps challenges. We will shine a light on Istio, a service mesh for K8s, and its role in enhancing the observability, security, and reliability of ML production pipelines. Attendees will learn how to leverage Kubeflow & MLFlow for hyperparameter tuning and distributed training, abstracting away Docker and Kubernetes complexities.
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