Build scalable MLOps with MLflow, KServe, Docker, and Kubernetes. Automate deployments, monitor models, and workflows.
What you will learn
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Deploy ML models to Kubernetes at scale using MLflow and KServe
Implement CI/CD pipelines and automate model updates using Kubernetes
Track experiments, perform hyperparameter tuning, and compare model versions with MLflow
Build, package, and monitor production-ready ML services with Docker, MLflow, and Kubernetes
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