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."The most valuable feature is the data wrangling, which is what I mainly use it for."
"The most useful features are the readily available extensions that speed up the work."
"Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"It's a very powerful and simple tool to use."
"What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"This open-source product can compete with category leaders in ELT software."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"The solution is really scalable."
"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."
"The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"The product's standout feature is a robust multi-file network with limited availability."
"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."
"The documentation is lacking and it could be better."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
"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 license is quite expensive for us."
"KNIME's documentation is not strong."
"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"The price could be improved."
"Operability with R could be improved."
"The regulatory requirements of the product need improvement."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"Integration with social media would be a valuable enhancement."
"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."
"It would be nice if the product offered more accessibility in general."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
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.
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