We performed a comparison between IBM Watson 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 solution is very easy to use."
"It is a stable, reliable product."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"It is a very stable and reliable solution."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"Watson Studio is very stable."
"It's been pretty scalable in terms of using multiple GPUs."
"The tool is very user-friendly."
"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."
"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."
"The solution's interface is very slow at times."
"The initial setup was complex."
"The decision making in their decision making feature is less good than other options."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"I want IBM's technical support team to provide more specific answers to queries."
"I think maybe the support is an area where it lacks."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"On the production side of things, having more frameworks would be helpful."
"The training of the models could be faster."
"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."
"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."
IBM Watson Studio is ranked 7th in AI Development Platforms with 13 reviews while PyTorch is ranked 11th in AI Development Platforms with 6 reviews. IBM Watson Studio is rated 8.2, while PyTorch is rated 8.6. The top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". On the other hand, the top reviewer of PyTorch writes "User-friendly, easy to learn, performs well, and is more advanced than other tools". IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI, Google Vertex AI and Amazon Comprehend, whereas PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform and Caffe. See our IBM Watson Studio vs. PyTorch report.
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