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Azure Machine Learning

Azure Machine Learning


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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Studio Pricing - Free


per month

Production Web API - Dev/Test


per month

Studio Pricing - Standard


per ML studio workspace/per month

Entry-level set up fee?

  • No setup fee


  • 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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Reviews and Ratings


Attribute Ratings


(1-4 of 4)
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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)
  • Text analysis.
  • Semantic text.
  • Search the semantic keywords.
  • Price
  • Product Features
  • Product Usability
  • Product Reputation
  • Analyst Reports
  • Don't know
Not sure
Score 2 out of 10
Vetted Review
Verified User
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. assumes the users are non-technical and with 10 mouse clicks is able to run a data science project.
Score 9 out of 10
Vetted Review
Verified User
Currently, it is used for our information technology sector to implement machine learning features in-house. The idea is to explore models and perform some experimentation. It's used to find Machine Learning solutions for internal use in the company. The Microsoft resources in this tool make it easier to use machine learning, like the use of visual interfaces and how they manage deployment.
  • Visual interface
  • Possibility to track the IDs and also get the results from it
  • Charts to collect data and quickly check for performance/problems
  • Hard to apply Python code and run
  • More models could be available
  • Tableau interface would be perfect
It is good to quickly and easily deploy a model for Machine Learning. It has a few coding aspects that enable machine learning that at first sight can be a problem for non-machine learning specialists. The system tries to gets the easiest results as possible.

It is less appropriate for complex systems and for detailed results to be analyzed.
  • It is easy to learn and construct, which impacts directly on productivity.
  • Good for experimentation and validation for simple models.
  • Has a use cost less than the best alternatives in the market.
It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.
Gabriel Chiararia | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
I was the president of an MBA class that used Azure ML to run analytics models. The tool was used by 40 students. We analyzed a few datasets to understand the tools, and afterward, we were able to create a few analytics products based on Azure ML.
  • User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
  • Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
  • Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
  • Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
  • Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
  • Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
  • Tableau interface: last time I checked there was no easy way to connect with Tableau.
  • Cloud based: You always need a good internet connection to use it.
Well suited:
- Run a machine learning model the fastest and easiest way;
- Working with an organization with no coding background;
- Trying to get the most of data the cheapest and easiest way possible;
- Introducing analytics and machine learning concepts to an organization or class;

Less appropriate:
- Running complex Machine Learning models;
- Visualizing data more deeply;
- Running new analytics models;
- Running heavy statistical models;
  • Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
  • Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
  • Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
The answer is quite simple: Microsoft Azure Machine Learning Workbench is the cheapest and most user friendly analytics tool I have ever seen! Unless you are running a team of data scientists, this is the tool to go. Most functions (marketing, sales, finance, supply chain, logistics, HR, R&D, etc.) could easily integrate Azure ML in its day to day activity.
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