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Azure Machine Learning
Azure Machine Learning

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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.

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What is Azure Machine Learning?

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.

What is Databricks?

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.
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          What are some alternatives to Azure Machine Learning and Databricks?
          Python
          Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
          Azure Databricks
          Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
          Amazon SageMaker
          A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
          Amazon Machine Learning
          This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
          NanoNets
          Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
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