Workflow Example in Databricks with MLflow

# In Databricks notebook - MLflow is pre-configured

from sklearn.ensemble import RandomForestRegressor

import mlflow

import mlflow.sklearn

# Auto-logging (Databricks enhancement)

mlflow.autolog()

# Train model - automatically tracked

model = RandomForestRegressor()

model.fit(X_train, y_train)

# Log additional metrics

mlflow.log_metric("custom_metric", value)

# Register model in MLflow Model Registry

mlflow.sklearn.log_model(

    model, 

    "revenue_model",

    registered_model_name="PlayStore_Revenue_Predictor"

)

Key Benefits of Using MLflow in Databricks

Zero Setup: MLflow is pre-installed and configured

Unified Interface: Experiments, models, and data in one platform

Scalability: Leverages Databricks' distributed computing

Collaboration: Shared experiments across teams

Production Ready: Easy model deployment and serving

Databricks is the commercial platform that provides the infrastructure and environment, while MLflow is the open-source framework (created by Databricks) for managing machine learning experiments and models. Using them together creates a powerful, integrated solution for enterprise ML workflows.