We performed a comparison between IBM Watson Machine Learning 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."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."
"It is has a lot of good features and we find the image classification very useful."
"The most valuable aspect of the solution's the cost and human labor savings."
"It has improved self-service and customer satisfaction."
"Scalability-wise, I rate the solution ten out of ten."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"It's a great option if you are fairly new and don't want to write too much code."
"ML Studio is very easy to maintain."
"The most valuable feature is data normalization."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"The solution facilitates our production."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"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."
"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."
"The supporting language is limited."
"In future releases, I would like to see a more flexible environment."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"The data cleaning functionality is something that could be better and needs to be improved."
"Operability with R could be improved."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"It could use to add some more features in data transformation, time series and the text analytics section."
"The solution's initial setup process is complicated."
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IBM Watson Machine Learning is ranked 9th in AI Development Platforms with 6 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 49 reviews. IBM Watson Machine Learning is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. 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 Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". IBM Watson Machine Learning is most compared with Google Cloud AI Platform, Azure OpenAI and TensorFlow, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our IBM Watson Machine Learning vs. Microsoft Azure Machine Learning Studio report.
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