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Microsoft Azure Machine Learning Studio OverviewUNIXBusinessApplication

Microsoft Azure Machine Learning Studio is #1 ranked solution in top AI Development Platforms and #4 ranked solution in top Data Science Platforms. PeerSpot users give Microsoft Azure Machine Learning Studio an average rating of 8 out of 10. Microsoft Azure Machine Learning Studio is most commonly compared to Databricks: Microsoft Azure Machine Learning Studio vs Databricks. The top industry researching this solution are professionals from a computer software company, accounting for 24% of all views.
What is Microsoft Azure Machine Learning Studio?

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

Microsoft Azure Machine Learning Will Help You:

  • Rapidly build and train models
  • Operationalize at scale
  • Deliver responsible solutions
  • Innovate on a more secure hybrid platform

With Microsoft Azure Machine Learning You Can:

  • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
  • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
  • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
  • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

Microsoft Azure Machine Learning Features:

  • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
  • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
  • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
  • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

Microsoft Azure Machine Learning Benefits:

  • It is fully integrated with Python and R SDKs.
  • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
  • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
  • It supports multiple model formats depending upon the job type.
  • It has automated model training and hyperparameter tuning with code-first and no-code options.
  • It supports data labeling projects.

Reviews from Real Users:

"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates

"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

"The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

"The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

Microsoft Azure Machine Learning Studio was previously known as Azure Machine Learning, MS Azure Machine Learning Studio.

Microsoft Azure Machine Learning Studio Buyer's Guide

Download the Microsoft Azure Machine Learning Studio Buyer's Guide including reviews and more. Updated: January 2022

Microsoft Azure Machine Learning Studio Customers

Walgreens Boots Alliance, Schneider Electric, BP

Microsoft Azure Machine Learning Studio Video

Archived Microsoft Azure Machine Learning Studio Reviews (more than two years old)

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Director at a tech services company with 1,001-5,000 employees
Real User
Easy to set up with good data normalization functionality
Pros and Cons
  • "The most valuable feature is data normalization."
  • "The data cleaning functionality is something that could be better and needs to be improved."

What is our primary use case?

Azure Machine Learning Studio works with our ERP solution.

What is most valuable?

The most valuable feature is data normalization.

What needs improvement?

The data cleaning functionality is something that could be better and needs to be improved.

There should be special pricing for developers so that they can learn this solution without paying full price.

For how long have I used the solution?

I have been using Azure Machine Learning Studio for more than two years.

What do I think about the stability of the solution?

This is a stable solution.

What do I think about the scalability of the solution?

I believe that it is scalable. At this time, we have not more than ten users. These include programmers, as well.

How are customer service and technical support?

I have been in contact with technical support and they are good. I am happy with their response time.

How was the initial setup?

The initial setup is straightforward and not too complex.

What about the implementation team?

We did the implementation by ourselves.

What's my experience with pricing, setup cost, and licensing?

From a developer's perspective, I find the price of this solution high. If somebody wants to learn how to use this platform then they have to spend money doing it. I know people who are interested in learning it but do not want to pay the full cost.

What other advice do I have?

Microsoft Azure Machine Learning Studio is a good solution that would recommend to others, but I would like to see more support and more information available for developers.

I would rate this solution an eight out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
Alexandre Akrour
CEO at Inosense
Real User
Good support for Azure services in pipelines, but deploying outside of Azure is difficult
Pros and Cons
  • "The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
  • "If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."

What is our primary use case?

We used this solution for defining new predictive models, such as recommendation systems, but also price elasticity models for fraud detection, and the classification of customers.

We are not using this solution regularly. We are now using Azure Databricks.

What is most valuable?

The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure. You just have to drag and drop the services into your pipeline, and it can be applied through the pipeline. It's very helpful for data scientists. If you don't have any special knowledge in data science, just to know that you want to consume a service, that's all you need.

They have a tool for data gathering from some social networking sites such as Twitter and Facebook, which is great.

What needs improvement?

If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.

One of the problems that we had was that you could only execute the model inside the machine learning environment. Comparing this to Databricks, if you create a pipeline, it could be in a notebook and you have all the code and then you can export your notebook to some other tool directly, for example in Jupyter and Spark. If you change tools then you won't lose your assets.

I would like to see improvements to make this solution more user-friendly.

They need to have some tools, like Apache Airflow, for helping to build workflows.

Better tools are needed to bring the data from existing storage into the environment where they can play with it and start to analyze what they already have, on-site. This is what the majority of people would like to do.

A feature that would be useful is to have some standard data transportation functions. They have ADF, Azure Data Factory, but it's a little bit heavy to manipulate. If they could have something more user-friendly, like Apache Airflow, it would be very nice.

For how long have I used the solution?

We have been using this solution for almost nine months.

What do I think about the stability of the solution?

This is a stable solution, although we have had problems with JavaScript. When you have many JavaScripts running, sometimes you have something that freezes, but we didn't know whether it was based on our network, the configuration, or the tools. It is difficult to identify the precise cause.

In general, there are no major issues.

What do I think about the scalability of the solution?

We never went into production because we switched to Azure Databricks. We did, however, try some performance testing and tried scaling some resources. The scalability of this solution is quite easy.

It is not difficult compared to some of the other tools that are available on Azure.

We have only five users including data engineers, data scientists, and one data DevOps engineer who was working with us on creating all of the DevOps pipelines for deploying all of our models.

How are customer service and technical support?

I have been in touch with technical support many times. The client I work for is a first-year client for them and we received some very useful support. The showed great willingness to help and they provided a lot of support for free.

We also had meetings with some experts on their data side and we had some free consultancy days given by Microsoft. It is called FastTrack and it is only available for some kinds of clients.

We are completely satisfied with the technical support.

Which solution did I use previously and why did I switch?

We did not use another solution prior to this one, but we now use Azure Databricks.

How was the initial setup?

The initial setup of this solution is straightforward.

The client site that we were working at had a proxy, and we were having a lot of trouble managing the rules inside the proxy because the Machine Learning Studio was not showing on the screen, in the browser, as it should. There are a lot of JavaScripts and this is a heavy client. There is a lot of feature logic performed on the client-side, such as the drag-and-drop. We had a lot of problems.

Besides that, once we fixed our network problem, it was straightforward.

What about the implementation team?

We implemented this solution on our own. The documents available on Microsoft Online made it quite easy.

What's my experience with pricing, setup cost, and licensing?

When we started using this solution, our licensing fees were approximately €1,000 (approximately $1,100 USD) monthly, but it was fluctuating. When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly. It was quite limited.

We expected the rate to be higher than this, at perhaps €10,000 (approximately $11,000 USD) per month, but it wasn't the case.

What other advice do I have?

Microsoft has increased the usability and the features since we first implemented this solution.

If I had to start this process over again, I would involve Microsoft earlier because they were great for providing support, as well as guidance on the architecture and what kind of stuff you can do with the tool, and what you should do with it. This was very helpful to orient the team to the right documentation and tutorials.

The second thing I would do is to start working with DevOps activity as soon as you can. We found ourselves redoing the same things many times, instead of having a DevOps pipeline to implement the stuff that we already stabilized, for example, and then not losing time.

The third thing is involving an integrator to help put together the big picture.

I would rate this solution a seven out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Learn what your peers think about Microsoft Azure Machine Learning Studio. Get advice and tips from experienced pros sharing their opinions. Updated: January 2022.
563,780 professionals have used our research since 2012.
Rolf Lindgren
CEO at a recruiting/HR firm with 1-10 employees
Real User
Visualizations are a key feature but it needs better operability with R

What is our primary use case?

Exploration of connections between biodata and psychometric test results.

What is most valuable?

Visualisation, and the possibility of sharing functions.

What needs improvement?

Operability with R could be improved.

For how long have I used the solution?

Less than one year.

What is our primary use case?

Exploration of connections between biodata and psychometric test results.

What is most valuable?

Visualisation, and the possibility of sharing functions.

What needs improvement?

Operability with R could be improved.

For how long have I used the solution?

Less than one year.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user848265
System Analyst at a financial services firm with 1,001-5,000 employees
Real User
Easy to deploy, drag and drop makes it easy to test various algorithms
Pros and Cons
  • "It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
  • "When you import the dataset you can see the data distribution easily with graphics and statistical measures."
  • "I would like to see modules to handle Deep Learning frameworks."

What is our primary use case?

The first time that I used this tool was in a project related to bike usage in the city of Boston. This project was part of a course that I concluded some months ago. In this project I used components to read data, for exploratory analysis, for steps of data munging, to split data, select hyperparameters, and some machine learning algorithms. In some steps I needed to insert R modules to apply some data transformation.

The target of this exercise was to predict bike usage in a day.

How has it helped my organization?

With this tool we could have all benefits of a cloud environment, such as scalability and access to machine-learning applications. These features are very important when you have large datasets and critical applications.

What is most valuable?

  • It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
  • When you import the dataset you can see the data distribution easily with graphics and statistical measures.
  • Easy to deploy and provide the project like a service.

What needs improvement?

For my project/exercise, this tools was perfect. I would like to see modules to handle Deep Learning frameworks.

For how long have I used the solution?

Less than one year.

What do I think about the stability of the solution?

No issues with stability.

What do I think about the scalability of the solution?

No issues with scalability.

How are customer service and technical support?

I didn’t need to use the support, but this tool has great documentation.

Which solution did I use previously and why did I switch?

Nowadays I use Python (Anaconda and Jupyter Notebook) and R (RStudio) to create my solutions and machine-learning models.

How was the initial setup?

It was very simple and straightforward. It is really simple to start building a project.

What's my experience with pricing, setup cost, and licensing?

There are two kinds of licenses, Free and Standard.

Free

  • 100 modules per experiment.
  • 1 hour per experiment.
  • 10GB storage space.
  • Single Node Execution/Performance.

Standard – $9.99/seat/month (probably a data scientist)

  • $1 per Studio Experimentation Hour. You will pay according to the number of hours your experiments run.
  • Unlimited modules per experiment.
  • Up to seven days per experiment, 24 hours per module.
  • Unlimited BYO storage space.
  • On-premises SQL data processing.
  • Multiple Nodes Execution/Performance.
  • Production Web API.
  • SLA.

What other advice do I have?

You will be able to create your machine-learning project and extract insights from it just by dragging and dropping components and adjusting some parameters. This tool is very user-friendly, so without a lot of programming skills you can build machine-learning projects. 

If you need more control over machine-learning modules you will need to add R or Python modules to create a customized machine-learning model.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user837534
Process Analyst
Real User
Split dataset, data visualization are helpful, but it needs integrated Pivot Table feature
Pros and Cons
  • "Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
  • "I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."

What is our primary use case?

My  primary use of ML Studio is to experiment with different algorithms and learn the techniques of machine learning. In the meantime, I have developed a few models related to finance. One of the predictive models I designed was an Invoice Discrepancy Prediction model using a Multiclass Neural Network algorithm. This model predicts if an invoice will have a variance of some sort when checked against the purchase order, before the payments are to be processed.

How has it helped my organization?

Thanks to the model I designed, the productivity of processing invoices has increased by over 11%, because the team members only verify invoices that are discrepancy-free now.

What is most valuable?

  • Split dataset
  • variety of algorithms
  • visualizing the data
  • drag and drop capability 

are the features I appreciate most. 

The capability to model the data by finding empty cells and filling missing values by deriving the median and more, are great features that makes the job way easier.

What needs improvement?

I personally would prefer if data could be tunneled to my model through a SAP ERP system. It also needs features of Excel, such as Pivot Tables, integrated.

For how long have I used the solution?

Less than one year.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user833565
Software Engineer
Real User
Enables quick creation of models for PoC in predictive analysis, but needs better ensemble modeling
Pros and Cons
  • "MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
  • "The graphical nature of the output makes it very easy to create PowerPoint reports as well."
  • "Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
  • "Enable creating ensemble models easier, adding more machine learning algorithms."

What is our primary use case?

To create quick data analytic experiments, without incurring the time and cost of spinning up servers, setting up Hadoop, etc. 

Although MLS makes it very easy to deploy the resulting machine-learning models via REST API, I primarily use MLS as a means to quickly spin up experiments and create proof of concept models.

How has it helped my organization?

Not widely adopted at my old workplace, I only used this to create quick proofs of concept to try to convince management of the viability of a project.

What is most valuable?

MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.

The easy drag and drop can create simple data science experiments. Low barrier to entry allows large number of candidates get started.

The graphical nature of the output makes it very easy to create PowerPoint reports as well.

What needs improvement?

Enable creating ensemble models easier, adding more machine learning algorithms.

For how long have I used the solution?

Less than one year.

What do I think about the stability of the solution?

Out of about 150-plus MLS experiments I have done, maybe two or three bugged out. Interestingly enough, those are the ones I can’t delete out of the account.

What do I think about the scalability of the solution?

Scalability, in terms of running experiments concurrently: Good. At max, I was able to run three different experiments concurrently.

Scalability in terms of deploying models: Unknown, I never deployed on Azure.  But I would guess REST API could probably easily handle a few K worth of hits per second, since that is how Microsoft is going to get paid.

How are customer service and technical support?

Never used it.

Which solution did I use previously and why did I switch?

The only other solution beyond this would be standard tools used by data scientists, like R, Python, etc. All of these would have a fairly high barrier to entry, requiring programming experience. The main selling point of MLS is the low barrier to entry, where even tech-savvy business people can use it.

How was the initial setup?

Simple. Create MLS live account (preferably paid ones), open MLS, done.

Caveat: Different organizations have different attitudes towards cloud use, especially with sensitive data. At Bridgestone, the hardest part was getting corporate approval to allow me to upload heavily treated, sensitive data to a cloud platform.

What's my experience with pricing, setup cost, and licensing?

To use MLS is fairly cheap. Even the paid account is something like $20/month,  unless you are provisioning large numbers of VMs for a Hadoop cluster.

The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API.

If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

Which other solutions did I evaluate?

R and Python.

Python + Pandas + scikit-learn: 

Pros: 

  • scikit-learn offers better performance for extremely large data sets
  • Large-data manipulation tools
  • Fairly good set of ML algorithms

Cons:

  • High barrier to entry, in terms of skill and knowledge
  • Fairly labor intensive to create large number of experiments

R + caret:

Pros:

  • Very good amount of ML algorithms (so many it may cause paralysis from too much choice, 200-plus algorithms)
  • Good performance, unless the data set is extremely large

Cons:

  • High barrier to entry
  • Data manipulation is a pain, you probably want to use another tool to pre-treat the data before loading it into R dataframes

What other advice do I have?

For data science professionals or programmers I would rate this solution a four out of 10. A major feature is missing: creating ensemble models. This can be achieved with the tool, but it's clumsy and slow.

For marketing or business professionals I would rate it an eight out of 10. It has a low barrier to entry, and can quickly create models that can be used for proof of concept and justify further investment in a full data science or Big Data project.

R and Python, in my mind, are still the way to go for a true data science/predictive analysis project. MLS's value is the ease of use and low barrier to entry. If one is not a programmer or statistician, MLS is a good way to get a project started, create a proof of concept.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Nitin-Jain
Senior Associate - Data Science at a consultancy with 51-200 employees
Real User
​It has helped in reducing the time involved for coding using R and/or Python
Pros and Cons
  • "Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
  • "It helps in building customized models, which are easy for clients to use​.​​"
  • "​It has helped in reducing the time involved for coding using R and/or Python."
  • "​It could use to add some more features in data transformation, time series and the text analytics section."
  • "Microsoft should also include more examples and tutorials for using this product.​"

What is our primary use case?

I have used it to deploy predictive models in the healthcare sector.

How has it helped my organization?

It has helped in reducing the time involved for coding using R and/or Python. Also, web service is quite easy and convenient to use for clients. 

What is most valuable?

Its ability to publish a predictive model as a web based solution and integrate R and Python codes are amazing. It helps in building customized models, which are easy for clients to use.

What needs improvement?

It could use to add some more features in data transformation, time series and the text analytics section. Microsoft should also include more examples and tutorials for using this product.

For how long have I used the solution?

One to three years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Jusiah Noah
Co-Founder at a tech services company with 51-200 employees
Real User
Top 20
Simplified development as scripts can be designed and implemented in real time

What is most valuable?

  • Feature-based selection
  • Compute
  • Data services.

How has it helped my organization?

Simplified development as scripts can be designed and implemented in real time.

What needs improvement?

I would like to see better prediction and analysis.

For how long have I used the solution?

We have used it for a few months.

What was my experience with deployment of the solution?

Good support is available when needed.

What do I think about the stability of the solution?

Stable at moment.

What do I think about the scalability of the solution?

There are no scalability issues at the moment as data volume is still low.

How are customer service and technical support?

Customer Service:

Customer service is good.

Technical Support:

Technical support is good.

Which solution did I use previously and why did I switch?

We did not use a solution previous to this one.

How was the initial setup?

It was complex to setup the workspace, but once it was done, we were good to go.

What about the implementation team?

We did the implementation in-house.

What was our ROI?

The ROI was 36%.

What's my experience with pricing, setup cost, and licensing?

The setup is a little complex, but it is worth it when it comes to security and efficiency.

Which other solutions did I evaluate?

We thought of doing this traditionally from scratch, but the Azure work space gives you the opportunity to utilize the environment and provide service in the shortest time possible.

What other advice do I have?

For the best, reliable results, it is the best solution to have in mind. Try it out.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
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