Databricks TL;DR: MLflow Lifecycle

MLflow Lifecycle Management

/tldr: Managing experiments, reproducibility, packaging, and deployment of ML models.

MLOps Tracking Deployment Open Source

MLflow Overview

MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It addresses four key challenges: **Tracking** experiments, **Reproducing** runs, **Packaging** code, and **Deploying** models.

1. MLflow Tracking (The Experiment Log)

Tracking is the core component, recording and querying all aspects of your ML experiments.

Parameters

Hyperparameters used (e.g., learning_rate, n_estimators).

Metrics

Evaluation scores (e.g., AUC, accuracy, MSE).

Artifacts

Output files, plots, serialized models, and images.

Tracking Code Example

                
import mlflow
import mlflow.sklearn

with mlflow.start_run():
    # Log a parameter
    mlflow.log_param("n_estimators", 100)
    
    # Train model (e.g., Random Forest)
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)
    
    # Log a metric
    mlflow.log_metric("roc_auc", roc_auc_score(y_test, predictions))
    
    # Log the entire model itself
    mlflow.sklearn.log_model(model, "random_forest_model")
                
            

2. MLflow Projects (The Packaging Standard)

Projects provide a standard format for packaging ML code, making it reusable and reproducible. This allows other data scientists to run your code without needing to know your specific environment setup.

**Artifacts:** A Project is typically a directory containing code, a Conda or requirements.txt file for dependencies, and an MLproject file.
**Reproducibility:** You can run an MLflow Project from a local path or a Git URI.

The `MLproject` file

                
name: MyDataScienceProject
                
# Define the environment needed for the project
conda_env: conda.yaml 

entry_points:
  main:
    parameters:
      alpha: {type: float, default: 0.5}
      l1_ratio: {type: float, default: 0.1}
    command: "python train.py --alpha {alpha} --l1-ratio {l1_ratio}"
                
            

3. MLflow Models and Model Registry

MLflow Models (The Deployment Format)

An MLflow Model is a standard, versioned format for packaging an ML model for deployment. It includes the model file, environment information, and a signature defining the inputs and outputs.

                
# Example: Deploying a model as a REST endpoint locally (for testing)
mlflow models serve -m "runs:/<run_id>/model_artifact_path" -p 5001

MLflow Model Registry (Centralized Governance)

The Registry is a centralized repository for managing the full lifecycle of a model, providing **versioning**, **stage transitions**, and **lineage**.

**Stages:** Models move through predefined stages: None → Staging → Production → Archived.
**Workflow:** Data Scientists register a promising model (from Tracking). MLOps Engineers review it in the Registry and promote it to Production.
**Deployment:** Production deployment systems (like Databricks Model Serving) consume models directly from the Production stage of the Registry.

MLflow standardizes the chaos of the ML lifecycle, making it scalable and governable.