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IBM Watson Machine Learning vs PyTorch comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM Watson Machine Learning
Ranking in AI Development Platforms
11th
Average Rating
8.0
Reviews Sentiment
7.5
Number of Reviews
7
Ranking in other categories
No ranking in other categories
PyTorch
Ranking in AI Development Platforms
8th
Average Rating
8.6
Reviews Sentiment
6.4
Number of Reviews
10
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 2.6%, up from 2.6% compared to the previous year. The mindshare of PyTorch is 1.5%, down from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Anurag Mayank - PeerSpot reviewer
A highly efficient solution that delivers the desired results to its users
I had not considered how the solution could be improved because I was focused on how it was helping me to solve my issues. 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. It would be beneficial to incorporate more AI into the solution.
Arucy Lionel - PeerSpot reviewer
Offers good backward compatible and simple to use
One of the things I really like about PyTorch is that it doesn't break with every update or deletion. That's why I switched from TensorFlow to PyTorch. I can still run the code I wrote three years ago in PyTorch on the latest version. It's very backward compatible, and it's also very simple to use. It's not overly technical, and the flow is pretty intuitive. And now that PyTorch 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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It is has a lot of good features and we find the image classification very useful."
"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."
"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 most valuable aspect of the solution's the cost and human labor savings."
"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."
"Scalability-wise, I rate the solution ten out of ten."
"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 product's initial setup phase is easy."
"The framework of the solution is valuable."
"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."
"It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install."
"It's been pretty scalable in terms of using multiple GPUs."
"We use PyTorch libraries, which are working well. It's very easy."
"The tool is very user-friendly."
 

Cons

"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."
"The supporting language is limited."
"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."
"Sometimes training the model is difficult."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"In future releases, I would like to see a more flexible environment."
"The product has breakdowns when we change the versions a lot."
"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."
"I would like to see better learning documents."
"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."
"The product has certain shortcomings in the automation of machine learning."
 

Pricing and Cost Advice

"I've only been using the free tier, but it's quite competitive on a service basis."
"The pricing model is good."
"PyTorch is open-sourced."
"The solution is affordable."
"PyTorch is open source."
"It is free."
"It is free."
"PyTorch is an open-source solution."
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Top Industries

By visitors reading reviews
Computer Software Company
14%
University
14%
Financial Services Firm
12%
Educational Organization
12%
Manufacturing Company
30%
Computer Software Company
11%
Healthcare Company
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about IBM Watson Machine Learning?
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.
What needs improvement with IBM Watson Machine Learning?
Sometimes training the model is difficult. We need to have at least a few different components to evaluate and understand the behavior of different users to have a very, very high accuracy in the m...
What needs improvement with PyTorch?
We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3. We also faced a few version compatibility issues with CUDA drivers.
 

Learn More

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Overview

Find out what your peers are saying about IBM Watson Machine Learning vs. PyTorch and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.