We performed a comparison between Microsoft Azure Machine Learning Studio and PyTorch based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"ML Studio is very easy to maintain."
"It's a great option if you are fairly new and don't want to write too much code."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"Visualisation, and the possibility of sharing functions are key features."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"The tool is very user-friendly."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"It's been pretty scalable in terms of using multiple GPUs."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"The framework of the solution is valuable."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"It could use to add some more features in data transformation, time series and the text analytics section."
"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."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."
"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 interface is a bit overloaded."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"On the production side of things, having more frameworks would be helpful."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The training of the models could be faster."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
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Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 53 reviews while PyTorch is ranked 10th in AI Development Platforms with 6 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while PyTorch is rated 8.6. 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 PyTorch writes "Offers good backward compatible and simple to use". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform, whereas PyTorch is most compared with OpenVINO, MXNet, Caffe, Google Vertex AI and Google Cloud AI Platform. See our Microsoft Azure Machine Learning Studio vs. PyTorch report.
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