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IBM Watson Machine Learning pros and cons

Vendor: IBM
4.0 out of 5

Pros & Cons summary

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Prominent pros & cons

PROS

IBM Watson Machine Learning enhances workflow management for organizations.
Cost and human labor savings are significant benefits of IBM Watson Machine Learning.
Image classification is a particularly useful feature of IBM Watson Machine Learning.
IBM Watson Machine Learning contributes to improved customer satisfaction and self-service.
Scalability is highly rated, making IBM Watson Machine Learning flexible for growth.

CONS

Scaling is limited in some use cases and needs improvement for easier expansion.
There is a need for more reports comparing data processes through different AI or machine learning capabilities.
Enhanced GPU processing power is necessary to boost performance with large data sets.
The supporting language options are restricted.
A more flexible environment is desired for improved use, particularly with Generative AI.
 

IBM Watson Machine Learning Pros review quotes

reviewer1499484 - PeerSpot reviewer
Jan 31, 2021
The most valuable aspect of the solution's the cost and human labor savings.
Anurag Mayank - PeerSpot reviewer
Mar 16, 2023
Scalability-wise, I rate the solution ten out of ten.
Natalia Raffo - PeerSpot reviewer
Jul 2, 2024
We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code.
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RichardXu - PeerSpot reviewer
Nov 5, 2020
The solution is very valuable to our organization due to the fact that we can work on it as a workflow.
MA
Nov 14, 2022
It has improved self-service and customer satisfaction.
reviewer2319402 - PeerSpot reviewer
Jan 3, 2024
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.
reviewer1485348 - PeerSpot reviewer
Feb 18, 2021
It is has a lot of good features and we find the image classification very useful.
 

IBM Watson Machine Learning Cons review quotes

reviewer1499484 - PeerSpot reviewer
Jan 31, 2021
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.
Anurag Mayank - PeerSpot reviewer
Mar 16, 2023
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.
Natalia Raffo - PeerSpot reviewer
Jul 2, 2024
Sometimes training the model is difficult.
Find out what your peers are saying about IBM, Google, TensorFlow and others in AI Development Platforms. Updated: December 2024.
824,067 professionals have used our research since 2012.
RichardXu - PeerSpot reviewer
Nov 5, 2020
Scaling is limited in some use cases. They need to make it easier to expand in all aspects.
MA
Nov 14, 2022
The supporting language is limited.
reviewer2319402 - PeerSpot reviewer
Jan 3, 2024
In future releases, I would like to see a more flexible environment.
reviewer1485348 - PeerSpot reviewer
Feb 18, 2021
They should add more GPU processing power to improve performance, especially when dealing with large amounts of data.