We performed a comparison between IBM Watson Machine Learning 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."Scalability-wise, I rate the solution ten out of ten."
"It is has a lot of good features and we find the image classification very useful."
"I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"It has improved self-service and customer satisfaction."
"The most valuable aspect of the solution's the cost and human labor savings."
"The framework of the solution is valuable."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"The tool is very user-friendly."
"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."
"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."
"The supporting language is limited."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."
"In future releases, I would like to see a more flexible environment."
"If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"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've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"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."
"On the production side of things, having more frameworks would be helpful."
"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."
IBM Watson Machine Learning is ranked 9th in AI Development Platforms with 6 reviews while PyTorch is ranked 11th in AI Development Platforms with 6 reviews. IBM Watson Machine Learning is rated 8.0, while PyTorch is rated 8.6. The top reviewer of IBM Watson Machine Learning writes "A highly efficient solution that delivers the desired results to its users". On the other hand, the top reviewer of PyTorch writes "Offers good backward compatible and simple to use". IBM Watson Machine Learning is most compared with Google Cloud AI Platform, Azure OpenAI and TensorFlow, whereas PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform and Caffe. See our IBM Watson Machine Learning vs. PyTorch report.
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