Databricks Model


                Databricks Model

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.

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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