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."This solution is easy to use and especially good at data preparation and wrapping."
"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."
"KNIME is easy to learn."
"We can deploy the solution in a cluster as well."
"It's a very powerful and simple tool to use."
"It's a huge tool with machine learning features as well."
"The product is open-source and therefore free to use."
"I've never had any problems with stability."
"The UI is very user-friendly and that AI is easy to use."
"The most valuable feature is data normalization."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"It's a great option if you are fairly new and don't want to write too much code."
"The solution is really scalable."
"It's easy to use."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"It helps in building customized models, which are easy for clients to use."
"Compared to the other data tools on the market, the user interface can be improved."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing."
"The predefined workflows could use a bit of improvement."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"Using the solution requires some specific learning which can take some time."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
"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."
"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."
"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."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"The regulatory requirements of the product need improvement."
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 53 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, Dataiku and IBM SPSS Modeler, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and IBM Watson Studio. See our KNIME vs. Microsoft Azure Machine Learning Studio report.
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