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.
"Visualisation, and the possibility of sharing functions are key features."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"It helps in building customized models, which are easy for clients to use."
"It's easy to deploy."
"Their support is helpful."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"TensorFlow provides Insights into both data and machine learning strategies."
"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 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."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
"The regulatory requirements of the product need improvement."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."
"The speed of deployment should be faster, as should testing."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"The initial setup time of the containers to run the experiment is a bit long."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"The solution is hard to integrate with the GPUs."
"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."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"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."
"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."
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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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