Build, Track, and Orchestrate AI Models with MLFlow & Kubeflow

This course takes you from ad-hoc Jupyter notebooks to production-style MLOps workflows tailored for AIOps use cases such as anomaly detection and incident prediction. You will learn how to make models reproducible, trackable, and deployable at scale using MLflow for experiment tracking and model packaging, and Kubeflow Pipelines for end-to-end orchestration on Kubernetes. Through hands-on labs, you will convert exploratory code into parameterized scripts, track experiments with MLflow and MinIO, deploy inference services to Kubernetes, and automate train → register → validate → deploy pipelines for AIOps workloads.