MLflow for Kubernetes: Deploy and Manage ML Models at Scale

Deploying machine learning models to production doesn’t have to be painful.
This comprehensive, hands-on course will teach you step-by-step how to make the leap from experiments to scalable, production-ready AI services using MLflow, Kubernetes, Docker, and KServe.

You will start by learning why Kubernetes and MLflow are essential for modern AI scalability, and how they can streamline the entire ML lifecycle — from tracking experiments to serving models in production environments. Through carefully designed lessons and real-world projects, you will build deep practical knowledge in:

• Setting up your environment — Install MLflow, configure Minikube, and deploy KServe on Kubernetes.

• Training and tracking models — Use MLflow Autologging and UI visualization to monitor your machine learning experiments.

• Hyperparameter tuning and model selection — Run randomized search experiments and compare model performance directly in MLflow.

• Packaging and serving models locally — Build Docker images and serve models with MLServer for quick local testing.

• Deploying models to Kubernetes at scale — Create KServe InferenceService YAML files and deploy models using kubectl with troubleshooting best practices.

• Performing inference and monitoring services — Send requests, interpret results, and monitor Kubernetes pods and logs for healthy service operations.

• Implementing production-level practices — Explore autoscaling, canary deployments, A/B testing, and use MLflow Model Registry for versioning and governance.

By the end of the course, you will be able to confidently operationalize ML models at scale, automate deployment workflows using CI/CD concepts, and manage the full lifecycle from training to production inference.

This course is ideal for ML engineers, MLOps specialists, and data scientists ready to move beyond notebooks and start building real-world, scalable ML systems.