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Azure Machine Learning

Azure Machine Learning

Overview

What is Azure Machine Learning?

Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.

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Recent Reviews
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

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Pricing

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Studio Pricing - Free

$0.00

Cloud
per month

Production Web API - Dev/Test

$0.00

Cloud
per month

Studio Pricing - Standard

$9.99

Cloud
per ML studio workspace/per month

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Details

What is Azure Machine Learning?

Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.

Azure Machine Learning Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(35)

Attribute Ratings

Reviews

(1-2 of 2)
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Score 8 out of 10
Vetted Review
Verified User
In the AI era, we need to build and deploy the machine learning model. Currently in our project is using the Azure Machine learning studio to preprocessing, cleaning, training and deployment of ML model as client requirement. As my knowledge in my team are using the Azure ML Studio. Currently, we are working to build the semantic text analysis of the documents.
  • Easy to create the experiment.
  • Easy to adopt the best algorithm.
  • Efficient way to deploy the model as a web service.
  • Centralized platform for the life cycle of machine learning goal.
  • Difficult to integrate the data for creating the model.
  • I feels it's costly to use it.
For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.
All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.
If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.
  • It reduce[d] the time and cost of the development, testing and deployment of the ML model.
  • Easy to track the model.
  • Nowaday[s] we are addicted with the cloud services.
The Azure Machine Learning Studio eliminates the complex tasks of data engineering and python coding for the data scientists to build models a simpler way. While SageMaker provide[s] a similar environment, [it] requires higher knowledge of data engineering. Even same for the Google cloud platform.
Easy and fastest way to develop, test, deploy and monitor the machine learning model.
- Easy to load the data set
-Drag and drop the process of the Machine learning life cycle.
I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.
Amazon SageMaker, Microsoft Power BI, IBM Watson Studio (formerly IBM Data Science Experience)
500
5
  • Text analysis.
  • Semantic text.
  • Search the semantic keywords.
No
  • Price
  • Product Features
  • Product Usability
  • Product Reputation
  • Analyst Reports
  • Don't know
Not sure
Score 2 out of 10
Vetted Review
Verified User
Incentivized
I create data science learning materials on Azure that require no coding. I use publicly available property data from Hong Kong island and surrounding areas. I teach my students how to preprocess the data, clean it up and create a hypothesis based on the type of data. We apply learning algorithms on the data and improve on the mode. The dataset was relatively small yet it took a while for the platform to get the analysis.
  • Adding python scripts
  • Pre-trained models
  • Case studies of industry projects
  • It would be great to have text tips that could ease new users to the platform, especially if an error shows up
  • Scenario-based documentation
  • Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason
Azure can be a more unified product. It feels like 10 different tech teams were building it but we're not talking to each other. An example is when the user needs to know what is the next step. Automatically saving a previous state is very helpful as new users are usually not aware of the functionality.
Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.
Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.
H20.ai assumes the users are non-technical and with 10 mouse clicks is able to run a data science project.
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