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Azure Machine Learning
Azure Machine Learning

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Sumo Logic
Sumo Logic

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Azure Machine Learning vs Sumo Logic: 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; Sumo Logic: Cloud Log Management for Application Logs and IT Log Data. Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

Azure Machine Learning and Sumo Logic are primarily classified as "Machine Learning as a Service" and "Log Management" 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, Sumo Logic provides the following key features:

  • Ability to collect data from on-premise sources, private/public/hybrid clouds, and SaaS/PaaS environments
  • Real-time continuous query engine that constantly updates dashboards and reports for immediate visualization
  • Anomaly detection engine that enables companies to proactively uncover events without writing rules

According to the StackShare community, Sumo Logic has a broader approval, being mentioned in 57 company stacks & 7 developers stacks; compared to Azure Machine Learning, which is listed in 12 company stacks and 8 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 Sumo Logic?

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.
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      What are some alternatives to Azure Machine Learning and Sumo Logic?
      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 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.
      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.
      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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