Construct scalable MLOps with MLflow, KServe, Docker, and Kubernetes. Automate deployments, monitor fashions, and workflows.
What you’ll be taught
Deploy ML fashions to Kubernetes at scale utilizing MLflow and KServe
Implement CI/CD pipelines and automate mannequin updates utilizing Kubernetes
Observe experiments, carry out hyperparameter tuning, and examine mannequin variations with MLflow
Construct, package deal, and monitor production-ready ML providers with Docker, MLflow, and Kubernetes
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