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 Azure Databricks workspace to run them.
These notebooks illustrate how to use Azure 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 Mosaic AI Model Training 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 |
| AI cookbook: Advanced RAG tutorial | Databricks Runtime ML | Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry |