Amazon Machine Learning vs Elasticsearch: What are the differences?
Developers describe Amazon Machine Learning as "Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology". 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. 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).
Amazon Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while Elasticsearch can be primarily classified under "Search as a Service".
Some of the features offered by Amazon Machine Learning are:
- Easily Create Machine Learning Models
- From Models to Predictions in Seconds
- Scalable, High Performance Prediction Generation Service
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 Amazon Machine Learning is used by Apli, Cymatic Security, and FetchyFox. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to Amazon Machine Learning, which is listed in 8 company stacks and 9 developer stacks.
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