Skip to main content
TrustRadius
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.

Read more
Recent Reviews
Read all reviews

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

Return to navigation

Pricing

View all pricing

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
Return to navigation

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
Return to navigation

Comparisons

View all alternatives
Return to navigation

Reviews and Ratings

(35)

Attribute Ratings

Reviews

(1-1 of 1)
Companies can't remove reviews or game the system. Here's why
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
Return to navigation