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."The most valuable aspect of the solution's the cost and human labor savings."
"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 is has a lot of good features and we find the image classification very useful."
"Scalability-wise, I rate the solution ten out of ten."
"It has improved self-service and customer satisfaction."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The most valuable feature is data normalization."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"Auto email and studio are great features."
"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."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"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."
"In future releases, I would like to see a more flexible environment."
"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."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"I would like to see modules to handle Deep Learning frameworks."
"There should be data access security, a role level security. Right now, they don't offer this."
"The price of the solution has room for improvement."
"I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
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 53 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 Google Vertex AI, Databricks, 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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