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 helps in building customized models, which are easy for clients to use."
"The most valuable feature is data normalization."
"Visualisation, and the possibility of sharing functions are key features."
"The solution facilitates our production."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"The solution is very easy to use, so far as our data scientists are concerned."
"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 is a framework that makes it really easy to use for deep learning."
"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."
"It's got quite a big community, which is useful."
"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."
"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."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"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."
"The data preparation capabilities need to be improved."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"There should be data access security, a role level security. Right now, they don't offer this."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"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."
"The solution is hard to integrate with the GPUs."
"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."
"TensorFlow Lite only outputs to C."
"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."
"Personally, I find it to be a bit too much AI-oriented."
"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 48 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 Databricks, Google Vertex AI, 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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