Microsoft Azure Machine Learning Studio vs OpenVINO comparison

Microsoft Azure Machine Learning Studio vs. OpenVINO

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Microsoft Azure Machine Lea...

Ranking in AI Development Platforms

7th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (9th)

OpenVINO

Ranking in AI Development Platforms

15th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2026, in the AI Development Platforms category, the mindshare of Microsoft Azure Machine Learning Studio is 3.3%, down from 5.8% compared to the previous year. The mindshare of OpenVINO is 1.7%, up from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Microsoft Azure Machine Learning Studio	3.3%
OpenVINO	1.7%
Other	95.0%

AI Development Platforms

Featured Reviews

reviewer2722962

Data Scientist

Platform accelerates model development, enhances collaboration, and offers efficient deployment

The best features Microsoft Azure Machine Learning Studio offers include deep integration with Python notebooks and Azure Data Lake, which allows me to import external data, and through the pipeline, I can build my models, performing what is called data injection for my model building, making that deep integration quite interesting to use. Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding. Microsoft Azure Machine Learning Studio has positively impacted my organization by reducing our project delivery times and increasing the pace at which we work, allowing us to focus on other more important tasks. Using Microsoft Azure Machine Learning Studio has reduced our model development time from approximately four hours to about two hours.

Read full review

Juan Huertas

Senior Data Scientist /Ai Engineer at Zantaz Data Resources

Empowers cost-effective model deployment on widely accessible hardware while needing cross-platform enhancements

What could be improved in OpenVINO is making the product more cross-platform. I know they are working with third-party plugins to extend the toolkit, and in this way, I can use it with NVIDIA GPUs or with other hardware because now it's primarily working in all Intel hardware. CPU, GPUs, TPUs, but only from Intel. If they make more cross-platform functionality, it would be great. It's difficult to make it work faster than the NVIDIA toolkit in their own GPUs. At least having the possibility and making it work faster than now in other hardware that is not from Intel provided would be beneficial.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Their support is helpful."

"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."

"THIS IS THE ONLY MODELING APPROACH THAT EVER WORKED THE VERY TIME I RAN IT!!"

"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."

"The integration with Azure services enhances workflow and meets my expectations."

"The solution is really scalable."

"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."

"The visualizations are great. It makes it very easy to understand which model is working and why."

More Microsoft Azure Machine Learning Studio pros

"The features for model comparison, the feature for model testing, evaluation, and deployment are very nice, and it can work almost with all the models."

"Intel's support team is very good."

"The benefit from using OpenVINO is that NVIDIA is dominating the market of GPUs and they set the price, so if I am able to run an LLM doing inference in commodity hardware, I am saving costs."

"The initial setup is quite simple."

"The solution's ability to stream data directly from camera inputs is the most valuable aspect for us."

"The runtime of OpenVINO is highly valuable for running different computer vision models."

"Compared to Jetson Nano or Jetson TX2, or Jetson Xavier, OpenVINO is a much more cost-effective solution."

"The inferencing and processing capabilities are quite beneficial for our requirements."

More OpenVINO pros

Cons

"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."

"I cannot comment on specific improvements yet as we are still exploring and need more time to identify the areas that require enhancements."

"The AutoML feature is very basic and they should improve it by using a more robust algorithm."

"I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else."

"The stability is questionable, given that Microsoft will be retiring the classic version of this product in 2024, and it's unclear how this will affect projects created on the classic version."

"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."

"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."

"In terms of improvement, I'd like to have more ability to understand the detailed impact of the variables on the model and their interactions."

More Microsoft Azure Machine Learning Studio cons

"It has some disadvantages because when you're working with very complex models, neural networks, if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model, it is difficult."

"It would be great if OpenVINO could convert new models into its format more quickly."

"At this point, the product could probably just use a greater integration with more machine learning model tools."

"The model optimization is a little bit slow — it could be improved."

"Scalability is a challenge with OpenVINO, particularly when I try to connect multiple streams of input or run multiple edge devices consecutively."

"I couldn't get it to run on my Raspberry Pi 4 because the software packages to download were no longer available."

"I think that it's not properly designed for scalability. It's designed for other purposes, specifically to be able to use Intel hardware and run inference using generative models or deep learning models in Intel hardware."

Pricing and Cost Advice

"The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten."

"We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees."

"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."

"The platform's price is low."

"My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."

"I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap."

"There is a license required for this solution."

"The solution operates on a pay-per-use model."

More Microsoft Azure Machine Learning Studio pricing and cost advice

"We didn't have to pay for any licensing with Intel OpenVINO. Everything is available on their site and easily downloadable for free."

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Manufacturing Company

Performing Arts

Outsourcing Company

Manufacturing Company

25%

Comms Service Provider

11%

Financial Services Firm

11%

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	23
Midsize Enterprise	6
Large Enterprise	32

No data available

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?

The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.

What needs improvement with Microsoft Azure Machine Learning Studio?

The initial setup can be a bit challenging for someone new, as the learning curve can be steep, but once I master the platform, I find it quite manageable. I would love to see the integration of a ...

What is your experience regarding pricing and costs for OpenVINO?

The pricing of OpenVINO is reasonable.

What needs improvement with OpenVINO?

I have heard good things about OpenVINO. It doesn't consume much current for external GPU usage. However, it has some downsides because I couldn't get it to run on my Raspberry Pi 4. While not spec...

What is your primary use case for OpenVINO?

I wanted to use OpenVINO for my Raspberry Pi to analyze my sleep with a night vision camera and to improve GPU performance on my Raspberry Pi. I would have used OpenVINO's Model Optimizer feature t...

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Compared 11% of the time

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Compared 10% of the time

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Product Reports

Microsoft Azure Machine Learning Studio

Download Microsoft Azure Machine Learning Studio product report

Microsoft Azure Machine Learning Studio report

OpenVINO

Download OpenVINO product report

Also Known As

Azure Machine Learning, MS Azure Machine Learning Studio

No data available

Overview

Microsoft Azure Machine Learning Studio offers a drag-and-drop interface, seamless integration with tools, and compatibility with multiple programming languages, making it user-friendly and efficient for developing and deploying machine-learning models.

This platform supports Python, R, and more, with automation features like AutoML, scalable resources, and cognitive services enhance data normalization and deployment. Users can easily create models, integrate with Azure services, and accelerate data science projects using its comprehensive library. Despite its benefits, improvements are suggested for cross-platform integration, enhanced data preparation, and clearer pricing strategies. Users also look for better deployment flexibility, more algorithm options, and examples. Expanded AI features, increased accessibility, and DevOps integration would further benefit its users.

What are the main features of Microsoft Azure Machine Learning Studio?

Drag-and-Drop Interface: Simplifies model creation and experimentation.
Integration with Tools: Works seamlessly with Azure services.
Compatibility with Languages: Supports Python, R, among others.
AutoML: Automates machine learning processes.
Scalable Resources: Provides adaptability in modeling.
Cognitive Services: Enhances data processing capabilities.

What benefits and ROI should users look for?

User-Friendly Design: Encourages accessibility for non-experts.
Seamless Deployment: Eases transition from model development to deployment.
Comprehensive Library: Accelerates data science processes.
Predictive Analytics: Aids in forecasting trends and behaviors.
Fraud Detection: Enhances security measures effectively.

In healthcare, finance, and retail, users apply Microsoft Azure Machine Learning Studio for developing machine learning models, predictive analytics, and deploying models efficiently. Its low-code interface supports experimentation for customer behavior prediction, data analytics, fraud detection, and automated machine learning, facilitating their business and research applications.

Microsoft

OpenVINO offers comprehensive tools for computer vision tasks, widely appreciating its compatibility with multiple hardware and frameworks. It facilitates seamless integration and supports direct camera streaming, making it versatile for device deployment and optimization.

OpenVINO is a powerhouse for machine learning enthusiasts, providing support for Intel CPUs and non-NVIDIA GPUs. Its compatibility spans multiple platforms, enhancing the deployment of models on diverse hardware. Users can efficiently convert and deploy models using OpenVINO's Model Zoo, coupled with support for frameworks like PyTorch and TensorFlow. Despite its strengths, there's room for improvement in conversion speed and better compatibility beyond Intel. It proves valuable for IoT applications, optimizing models efficiently for edge devices.

What are the standout features of OpenVINO?

Runtime for computer vision models: Offers reliable runtime support for various AI models.
Support for Apple silicon: Extends compatibility to Apple hardware.
Ease of integration: Simplifies the incorporation into existing workflows.
Direct camera streaming: Allows direct integration with camera systems.
Model conversion feature: Facilitates seamless model adaptation and deployment.
Model Zoo: Provides access to a collection of pre-trained models.
Compatibility with frameworks: Integrates with PyTorch, TensorFlow.

What benefits should users look for?

Hardware cost savings: Reduces dependency on NVIDIA, offering inexpensive alternatives.
Custom deployment: Enables tailored model deployment.
Versatility: Supports numerous frameworks, enhancing flexibility.
Efficient IoT processing: Optimizes performance on edge devices.

OpenVINO's applications span multiple industries, notably in video analytics and IoT. Users utilize its capabilities to build sophisticated systems for real-time analytics, model optimization for low-power devices, and intelligent edge processing. Projects like sleep analysis on Raspberry Pi or surveillance systems showcase its diverse implementations, highlighting its capacity to enhance industry-specific solutions.

Intel

Sample Customers

Walgreens Boots Alliance, Schneider Electric, BP

Information Not Available

Microsoft Azure Machine Learning Studio vs. OpenVINO