We performed a comparison between KNIME and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same time."
"The solution allows for sharing model designs and model operations with other data analysts."
"I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data."
"From a user-friendliness perspective, it's a great tool."
"Easy to use, stable, and powerful."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"It is a stable solution...It is a scalable solution."
"The initial setup is very simple and straightforward."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"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 solution facilitates our production."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"The pricing needs improvement."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved."
"KNIME's documentation is not strong."
"Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area."
"The ability to handle large amounts of data and performance in processing need to be improved."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"The solution's initial setup process is complicated."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"The initial setup time of the containers to run the experiment is a bit long."
"Operability with R could be improved."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
KNIME is ranked 4th in Data Science Platforms with 50 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 49 reviews. KNIME is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". 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". KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Databricks, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Amazon SageMaker. See our KNIME vs. Microsoft Azure Machine Learning Studio report.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.