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.
Azure Machine Learning is a tool in the Machine Learning as a Service category of a tech stack.
Who uses Azure Machine Learning?
31 companies reportedly use Azure Machine Learning in their tech stacks, including Microsoft, Infrastructure, and Data-Driven Services.
160 developers on StackShare have stated that they use Azure Machine Learning.
Azure Machine Learning's Features
- Designed for new and experienced users
- Proven algorithms from MS Research, Xbox and Bing
- First class support for the open source language R
- Seamless connection to HDInsight for big data solutions
- Deploy models to production in minutes
- Pay only for what you use. No hardware or software to buy
Azure Machine Learning Alternatives & Comparisons
What are some alternatives to Azure Machine Learning?
See all alternatives
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Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
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.
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.