We performed a comparison between Dataiku 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 of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"The solution is quite stable."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The most valuable feature is the set of visual data preparation tools."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Data Science Studio's data science model is very useful."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"The solution is very easy to use, so far as our data scientists are concerned."
"It has helped in reducing the time involved for coding using R and/or Python."
"Their support is helpful."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The ability to have charts right from the explorer would be an improvement."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"It could use to add some more features in data transformation, time series and the text analytics section."
"The platform's integration feature could be better."
"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."
"They should have a desktop version to work on the platform."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"Using the solution requires some specific learning which can take some time."
"The regulatory requirements of the product need improvement."
"Operability with R could be improved."
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Dataiku is ranked 7th in Data Science Platforms with 7 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. Dataiku is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". 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". Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and Amazon SageMaker, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and KNIME. See our Dataiku vs. Microsoft Azure Machine Learning Studio report.
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