MLflow Onboarding MLflow supports two broad use cases that require different onboarding paths: - GenAI applications and agents : LLM-powered apps, chatbots, RAG pipelines, tool-calling agents. Key MLflow features include tracing for observability, evaluation with LLM judges, and prompt management , among others. - Traditional ML / deep learning models : scikit-learn, PyTorch, TensorFlow, XGBoost, etc. Key MLflow features include experiment tracking (parameters, metrics, artifacts), model logging , and model deployment , among others. Determining which use case applies is the first and most im…