mlflow-onboarding

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:

GenAI indicators (any of these suggest GenAI):

• Imports from LLM client libraries: openai, anthropic, google.generativeai, google.genai, langchain, langchain_openai, langgraph, llamaindex, litellm, autogen, crewai, dspy
• Imports from MLflow GenAI modules: mlflow.genai, mlflow.tracing, mlflow.openai, mlflow.langchain
• Usage of chat completions, embeddings, or agent frameworks
• Prompt templates or prompt engineering code

Traditional ML indicators (any of these suggest ML):

• Imports from ML frameworks: sklearn, torch, tensorflow, keras, xgboost, lightgbm, catboost, statsmodels, scipy
• Imports from MLflow ML modules: mlflow.sklearn, mlflow.pytorch, mlflow.tensorflow
• Model training loops, .fit() calls, hyperparameter tuning code
• Dataset loading with tabular/image/time-series data

# Search for GenAI indicators
grep -rl --include='*.py' -E '(import openai|import anthropic|from langchain|from langgraph|import litellm|from mlflow\.genai|from mlflow\.tracing|mlflow\.openai|mlflow\.langchain|ChatCompletion|chat\.completions)' .

# Search for ML indicators
grep -rl --include='*.py' -E '(from sklearn|import torch|import tensorflow|import keras|import xgboost|import lightgbm|mlflow\.sklearn|mlflow\.pytorch|mlflow\.tensorflow|\.fit\()' .