Tutorials: Get started with AI and machine learning

The notebooks in this section are designed to get you started quickly with AI and machine learning on Mosaic AI. You can import each notebook to your Databricks workspace to run them.

These notebooks illustrate how to use Databricks throughout the AI lifecycle, including data loading and preparation; model training, tuning, and inference; and model deployment and management.

Classical ML tutorials

Notebook	Requirements	Features
End-to-end example	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, XGBoost
Deploy and query a custom model	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
Machine learning with scikit-learn	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
Machine learning with MLlib	Databricks Runtime ML	Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API
Deep learning with TensorFlow Keras	Databricks Runtime ML	Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry

AI tutorials

Notebook	Requirements	Features
Get started querying LLMs	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, XGBoost
Query OpenAI external model endpoints	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
Create and deploy a Foundation Model Fine-tuning run	Databricks Runtime ML	Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
10-minute RAG demo	Databricks Runtime ML	Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API
Generative AI tutorial	Databricks Runtime ML	Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry