Overview

Azure Databricks is an Apache Spark-based analytics platform optimized for the Azure cloud services platform. It offers a collaborative workspace where data scientists, data engineers, and machine learning engineers can build, train, and deploy machine learning models with speed and agility.

Key benefits include:

  • End-to-end machine learning lifecycle management
  • Scalable compute powered by Apache Spark
  • Seamless integration with Azure services like Azure Blob Storage, Azure Data Lake Storage, and Azure Machine Learning
  • Collaborative notebooks for interactive development
  • MLflow integration for model tracking and management
  • Optimized performance for large-scale data processing and model training

Key Features

Collaborative Workspace

Enable teams to work together on data science projects using notebooks, dashboards, and shared clusters.

Learn more about notebooks

Scalable Compute

Dynamically scale your Spark clusters to handle massive datasets and complex computations for training and inference.

Explore cluster management

Data Integration

Easily connect to and process data from various Azure data sources, including data lakes and databases.

Connect to data sources

MLflow Integration

Track experiments, package code, and deploy models using the integrated MLflow platform for robust MLOps.

Discover MLflow capabilities

Getting Started

Begin your journey with Azure Databricks by creating a workspace, setting up your compute resources, and loading your data. Our comprehensive documentation and tutorials will guide you through each step.

Create an Azure Databricks Workspace View Quickstart Guide

Use Cases

Azure Databricks is instrumental in various AI and ML applications, including:

  • Large-scale model training
  • Feature engineering
  • Real-time data processing for ML
  • Predictive analytics
  • Deep learning
  • Natural Language Processing (NLP)
  • Computer Vision