---
title: Machine learning on Databricks | Databricks on AWS
source: https://docs.databricks.com/aws/en/machine-learning/
ingestedAt: "2026-06-18T08:07:59.979Z"
---

Build, deploy, and manage machine learning applications on Databricks. The integrated platform unifies the entire ML lifecycle from data preparation to production monitoring.

Looking for generative AI and AI agents? See [Build AI agents on Databricks](https://docs.databricks.com/aws/en/agents/).

## Get started[​](#get-started "Direct link to Get started")

Try a quickstart, prepare your data, or build a low-code model.

*   *   [Get started: Build your first machine learning model on Databricks](https://docs.databricks.com/aws/en/getting-started/ml-get-started)
    *   Build a simple classification model with scikit-learn end-to-end.
*   *   [AutoML](https://docs.databricks.com/aws/en/machine-learning/automl/)
    *   Automatically build high-quality models with minimal code using automated feature engineering and hyperparameter tuning.
*   *   [Load data for machine learning and deep learning](https://docs.databricks.com/aws/en/machine-learning/load-data/)
    *   Load and prepare data for ML and deep learning workflows.
*   *   [Train recommender models](https://docs.databricks.com/aws/en/machine-learning/train-recommender-models)
    *   Train a recommender model with the two-tower or DLRM architecture.

## Train classic machine learning models[​](#-train-classic-machine-learning-models "Direct link to -train-classic-machine-learning-models")

Create machine learning models with automated tools and collaborative development environments.

*   *   [Databricks Runtime for ML](https://docs.databricks.com/aws/en/machine-learning/databricks-runtime-ml)
    *   Pre-configured clusters with scikit-learn, XGBoost, MLflow, and other ML libraries, plus support for deep learning frameworks.
*   *   [MLflow tracking](https://docs.databricks.com/aws/en/mlflow/tracking)
    *   Track experiments, compare model performance, and manage the complete model development lifecycle.
*   *   [Feature engineering](https://docs.databricks.com/aws/en/machine-learning/feature-store/)
    *   Create, manage, and serve features with automated data pipelines and feature discovery.
*   *   [Databricks notebooks](https://docs.databricks.com/aws/en/notebooks/)
    *   Collaborative development environment with support for Python, R, Scala, and SQL for ML workflows.

## Train deep learning models[​](#train-deep-learning-models "Direct link to Train deep learning models")

Use managed compute and built-in frameworks to develop deep learning models.

*   *   [Distributed training](https://docs.databricks.com/aws/en/machine-learning/train-model/distributed-training/)
    *   Examples of distributed deep learning using Ray, TorchDistributor, and DeepSpeed.
*   *   [AI Runtime](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/)
    *   Serverless GPU compute for custom deep learning training and inference workloads.
*   *   [DL best practices](https://docs.databricks.com/aws/en/machine-learning/train-model/dl-best-practices)
    *   Guidance for framework choice, data loading, distributed scaling, and managing the deep learning model lifecycle.
*   *   [PyTorch](https://docs.databricks.com/aws/en/machine-learning/train-model/pytorch)
    *   Single-node and distributed training using PyTorch.

## Deploy and serve models[​](#deploy-and-serve-models "Direct link to Deploy and serve models")

Deploy models to production with scalable endpoints, real-time inference, and enterprise-grade monitoring.

*   *   [Model Serving](https://docs.databricks.com/aws/en/machine-learning/model-serving/)
    *   Deploy custom models and LLMs as scalable REST endpoints with automatic scaling and GPU support.
*   *   [AI Gateway](https://docs.databricks.com/aws/en/ai-gateway/)
    *   Govern and monitor access to models served on Databricks with usage tracking, payload logging, and security controls.
*   *   [External models](https://docs.databricks.com/aws/en/generative-ai/external-models/)
    *   Integrate third-party models hosted outside Databricks with unified governance and monitoring.
*   *   [Foundation model APIs](https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/)
    *   Access and query state-of-the-art open models hosted by Databricks.

## Monitor and govern ML systems[​](#monitor-and-govern-ml-systems "Direct link to Monitor and govern ML systems")

Ensure model quality, data integrity, and compliance with comprehensive monitoring and governance tools.

*   *   [Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog/)
    *   Govern data, features, models, and functions with unified access control, lineage tracking, and discovery.
*   *   [Data profiling](https://docs.databricks.com/aws/en/data-governance/unity-catalog/data-quality-monitoring/data-profiling/)
    *   Monitor data quality, model performance, and prediction drift with automated alerts and root cause analysis.
*   *   [Anomaly detection](https://docs.databricks.com/aws/en/data-governance/unity-catalog/data-quality-monitoring/anomaly-detection/)
    *   Monitor data freshness and completeness at the catalog level.
*   *   [MLflow for Models](https://docs.databricks.com/aws/en/mlflow/)
    *   Track experiments, manage models in Unity Catalog, deploy, and evaluate machine learning models throughout the development lifecycle.

## Productionize ML workflows[​](#productionize-ml-workflows "Direct link to Productionize ML workflows")

Scale machine learning operations with automated workflows, CI/CD integration, and production-ready pipelines.

*   *   [Models in Unity Catalog](https://docs.databricks.com/aws/en/machine-learning/manage-model-lifecycle/)
    *   Use the model registry in Unity Catalog for centralized governance and to manage the model lifecycle, including deployments.
*   *   [Lakeflow Jobs](https://docs.databricks.com/aws/en/jobs/)
    *   Build automated workflows and production-ready ETL pipelines for ML data processing.
*   *   [Ray on Databricks](https://docs.databricks.com/aws/en/machine-learning/ray/)
    *   Scale ML workloads with distributed computing for large-scale model training and inference.
*   *   [MLOps workflows](https://docs.databricks.com/aws/en/machine-learning/mlops/mlops-workflow)
    *   Implement end-to-end MLOps with automated training, testing, and deployment pipelines.
