Azure Machine Learning vs Elasticsearch: What are the differences?
Developers describe Azure Machine Learning as "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. On the other hand, Elasticsearch is detailed as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Azure Machine Learning and Elasticsearch are primarily classified as "Machine Learning as a Service" and "Search as a Service" tools respectively.
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, Elasticsearch provides the following key features:
- Distributed and Highly Available Search Engine.
- Multi Tenant with Multi Types.
- Various set of APIs including RESTful
Elasticsearch is an open source tool with 41.9K GitHub stars and 14K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.
Instacart, Slack, and Stack Exchange are some of the popular companies that use Elasticsearch, whereas Azure Machine Learning is used by Microsoft, Bluebeam Software, and Petra. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to Azure Machine Learning, which is listed in 12 company stacks and 8 developer stacks.
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