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 ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"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."
"Clear view of the data at every step of ETL process enables changing the flow as needed."
"There are a lot of connectors available in KNIME."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"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."
"KNIME is quite scalable, which is one of the most important features that we found."
"This open-source product can compete with category leaders in ELT software."
"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."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"Azure's AutoML feature is probably better than the competition."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"Auto email and studio are great features."
"The product's standout feature is a robust multi-file network with limited availability."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"The predefined workflows could use a bit of improvement."
"The data visualization part is the area most in need of improvement."
"The ability to handle large amounts of data and performance in processing need to be improved."
"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 pricing needs improvement."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."
"I would like to see modules to handle Deep Learning frameworks."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
"The product must improve its documentation."
"The solution cannot connect to private block storage."
"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."
"I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important."
"The platform's integration feature could be better."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
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 51 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 Databricks, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Amazon SageMaker. See our KNIME vs. Microsoft Azure Machine Learning Studio report.
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