Machine Learning Models on Kubernetes focuses on deploying, scaling, and managing ML models using container orchestration for production-grade environments. It enables organizations to run machine learning workloads efficiently with high availability, scalability, and fault tolerance. This training explains core concepts such as Kubernetes architecture, pods, deployments, services, and autoscaling for ML inference. It also covers containerization, model serving frameworks, CI/CD integration, and monitoring strategies. You will learn how enterprises use Kubernetes to host ML models for real-time predictions and batch processing at scale. The course also highlights best practices for building resilient, secure, and high-performance machine learning deployment systems.
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