ML Engineering & MLOps Challenges

ML Engineering & MLOps challenges put you inside the work of getting models out of notebooks and into production. You'll develop skills in building ML Pipelines, Model Packaging and Model Deployment, and understanding the gap between Training vs Serving, while tracking work in MLflow.

From there you'll handle the harder edges — Model Monitoring, Drift detection & auto-retraining, Kubeflow pipelines, Edge Deployment, and ONNX optimization — running Weights & Biases experiment tracking and Production ML deployment the way real MLOps teams do. Each challenge you solve earns a verified credential you can share with recruiters.