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 most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The most valuable feature is data normalization."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"The product supports open-source tools."
"The solution is very easy to use, so far as our data scientists are concerned."
"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."
"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."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"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 regulatory requirements of the product need improvement."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"Operability with R could be improved."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"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."
"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."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"The training of the models could be faster."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
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Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 49 reviews while PyTorch is ranked 11th 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 Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform, whereas PyTorch is most compared with OpenVINO, MXNet, Google Cloud AI Platform, Caffe and Google Vertex AI. See our Microsoft Azure Machine Learning Studio vs. PyTorch report.
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