AI and machine learning tutorials

The tutorials in this section illustrate how to use Databricks throughout the AI lifecycle for classical ML and gen AI workloads.

If you're new to AI on Databricks, see Try generative AI and machine learning on Databricks for a curated list of notebooks and tutorials designed to quickly get you started with AI.

Classical ML tutorials

You can import each notebook to your Databricks workspace to run them.

Notebook	Features
Deploy and query a custom model	Unity Catalog, classification model, MLflow, model serving, Hugging Face transformer, PyFunc model
Machine learning with scikit-learn	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Optuna and MLflow
Machine learning with MLlib	Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API
Deep learning with TensorFlow Keras	Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry

Deep learning tutorial

Notebook	Requirements	Features
End-to-end PyTorch example	Databricks Runtime ML	Unity Catalog, PyTorch, MLflow, automated hyperparameter tuning with Optuna and MLflow

Gen AI tutorials

You can import each notebook to your Databricks workspace to run them.

Notebook	Features
Query OpenAI external model endpoints	OpenAI API, MLflow, External models, Databricks Secrets
Create and deploy a Foundation Model Fine-tuning run	Foundation Model fine-tuning, databricks_genai SDK
Build, evaluate, and deploy production-grade AI agents	Mosaic AI Agent Framework, Agent Evaluation, MLflow, synthetic data