We performed a comparison between Hugging Face and Microsoft Azure Machine Learning Studio 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."My preferred aspects are natural language processing and question-answering."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
"The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"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 Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."
"The solution is very easy to use, so far as our data scientists are concerned."
"The solution is very fast and simple for a data science solution."
"Visualisation, and the possibility of sharing functions are key features."
"The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily."
"Implementing a cloud system to showcase historical data would be beneficial."
"I would like to see modules to handle Deep Learning frameworks."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"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 speed of deployment should be faster, as should testing."
"The data cleaning functionality is something that could be better and needs to be improved."
"The solution cannot connect to private block storage."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
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Hugging Face is ranked 7th in AI Development Platforms with 3 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 53 reviews. Hugging Face is rated 9.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Hugging Face writes "A comprehensive natural language processing ecosystem offering a diverse range of pre-trained models and a collaborative platform". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Hugging Face is most compared with Google Vertex AI, Azure OpenAI, Replicate, Google Cloud AI Platform and DataRobot, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our Hugging Face vs. Microsoft Azure Machine Learning Studio report.
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