Azure Machine Learning vs Databricks: What are the differences?
Azure Machine Learning: A fully-managed cloud service for predictive analytics. Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning; Databricks: A unified analytics platform, powered by Apache Spark. Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Azure Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while Databricks can be primarily classified under "General Analytics".
Some of the features offered by Azure Machine Learning are:
- Designed for new and experienced users
- Proven algorithms from MS Research, Xbox and Bing
- First class support for the open source language R
On the other hand, Databricks provides the following key features:
- Built on Apache Spark and optimized for performance
- Reliable and Performant Data Lakes
- Interactive Data Science and Collaboration
Microsoft, Hebe Works, and Bluebeam Software are some of the popular companies that use Azure Machine Learning, whereas Databricks is used by Auto Trader, Snowplow Analytics, and Fairygodboss. Azure Machine Learning has a broader approval, being mentioned in 23 company stacks & 38 developers stacks; compared to Databricks, which is listed in 7 company stacks and 4 developer stacks.