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Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart…
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 important step. The onboarding path, quickstart tutorials, and integration steps differ significantly between the two. Step 1: Determine the Use Case Before recommending tutorials or integration steps, determine which use case the user is working on. Use the signals below, checking them in order. If the signals are ambiguous or absent, you MUST ask the user directly. Signal 1: Check the Codebase Search the user's project for imports and usage patterns that indicate the use case:
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