Databricks is a unified data and AI platform that offers various tools and services for developing, deploying, and managing machine learning models.

Model Development

• AutoML: Databricks AutoML allows users to automatically build, train, and tune machine learning models with minimal manual intervention. This is particularly useful for users with limited machine-learning expertise.
• MLflow: Databricks integrates with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle. This includes tracking experiments, model versioning, and model deployment.

Model Deployment

• Model Serving: Databricks Model Serving provides a unified interface to deploy, manage, and monitor machine learning models as REST APIs. It supports custom models and pre-trained models from the Databricks Model Registry.

Other Features

• Feature Store: Databricks Feature Store enables users to define, store, and share features across different teams and projects. This helps ensure consistency and improve model performance.
• Foundation Model APIs: Databricks provides APIs to access and use pre-trained foundation models (like large language models) for generative AI applications.