We compared Microsoft Azure Machine Learning Studio and TensorFlow based on our user's reviews in several parameters.
In summary, Microsoft Azure Machine Learning Studio is praised for its user-friendly interface, seamless integration with other Azure services, reliable performance, and excellent support and documentation. On the other hand, TensorFlow is valued for its versatility, efficiency, extensive library of tools, and user-friendly interface. Users appreciate the flexible pricing options of both platforms, with Microsoft Azure Machine Learning Studio offering reasonable setup costs and TensorFlow providing a variety of pricing options suited to different needs. However, users have identified areas for improvement in both platforms, such as enhancing the user interface and documentation for Microsoft Azure Machine Learning Studio, and improving ease of use, documentation, and performance optimization for TensorFlow.
Features: Microsoft Azure Machine Learning Studio is praised for its user-friendly interface, extensive range of tools and algorithms, seamless integration with Azure services, reliable and scalable performance, and excellent support and documentation. On the other hand, TensorFlow is highly valued for its versatility, usability, efficiency, extensive library of tools and functions, flexibility in building and training deep learning models, user-friendly interface, well-documented resources, efficient utilization of hardware resources, and pre-built models, algorithms, and visualization tools.
Pricing and ROI: The setup cost for Microsoft Azure Machine Learning Studio is reasonable, with users finding the licensing process straightforward. In comparison, TensorFlow offers flexible pricing options suited to different needs, with a straightforward setup cost that users find hassle-free. TensorFlow's licensing is perceived as fair and transparent, instilling confidence in its usage., User feedback indicates positive ROI for both Microsoft Azure Machine Learning Studio and TensorFlow. Azure ML Studio is praised for its reliability, user-friendliness, and seamless data integration, while TensorFlow users have reported significant value and favorable outcomes.
Room for Improvement: Microsoft Azure Machine Learning Studio could improve its user interface to be more user-friendly. It also needs better documentation and collaboration features. In contrast, TensorFlow could enhance its ease of use, installation process, and performance. It should provide more comprehensive tutorials, visualization capabilities, and debugging tools.
Deployment and customer support: In the user reviews for Microsoft Azure Machine Learning Studio, there is variability in the reported durations for deployment, setup, and implementation. Some users mention different time frames for these phases, while others suggest they occur within the same period. However, user reviews for TensorFlow indicate a wider range of durations, with deployment taking a few weeks or a month, and setup ranging from a few days to a month. This suggests that Azure Machine Learning Studio may have a more consistent or efficient process for establishing a new tech solution compared to TensorFlow., Microsoft Azure Machine Learning Studio offers excellent assistance and guidance, with prompt and efficient support. Users praise the reliable and knowledgeable customer service. TensorFlow also provides highly praised customer service, ensuring prompt and helpful responses and a knowledgeable support staff.
The summary above is based on 29 interviews we conducted recently with Microsoft Azure Machine Learning Studio and TensorFlow users. To access the review's full transcripts, download our report.
"It's easy to use."
"The most valuable feature is its compatibility with Tensorflow."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"The product supports open-source tools."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"The interface is very intuitive."
"Their support is helpful."
"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions."
"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."
"The price could be improved."
"The platform's integration feature could be better."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"The data cleaning functionality is something that could be better and needs to be improved."
"They should have a desktop version to work on the platform."
"The speed of deployment should be faster, as should testing."
"There should be data access security, a role level security. Right now, they don't offer this."
"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."
"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"Personally, I find it to be a bit too much AI-oriented."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."
"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 53 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while TensorFlow is rated 9.0. The top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, Google Cloud AI Platform and Dataiku, whereas TensorFlow is most compared with Google Vertex AI, OpenVINO, Hugging Face, Azure OpenAI and IBM Watson Machine Learning. See our Microsoft Azure Machine Learning Studio vs. TensorFlow report.
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