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.
"Auto email and studio are great features."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The UI is very user-friendly and that AI is easy to use."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"ML Studio is very easy to maintain."
"It's easy to deploy."
"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 most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"It's got quite a big community, which is useful."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."
"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."
"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."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"They should have a desktop version to work on the platform."
"The data cleaning functionality is something that could be better and needs to be improved."
"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."
"The solution should be more customizable. There should be more algorithms."
"The data preparation capabilities need to be improved."
"The speed of deployment should be faster, as should testing."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"The initial setup time of the containers to run the experiment is a bit long."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"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."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"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 doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 50 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 Data Science Studio, whereas TensorFlow is most compared with Google Vertex AI, OpenVINO, IBM Watson Machine Learning, Hugging Face and Azure OpenAI. See our Microsoft Azure Machine Learning Studio vs. TensorFlow report.
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