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 solution is quite stable."
"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."
"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."
"Data Science Studio's data science model is very useful."
"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 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."
"Their support is helpful."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"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's easy to use."
"The initial setup is very simple and straightforward."
"Azure's AutoML feature is probably better than the competition."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"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."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."
"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."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"The solution cannot connect to private block storage."
"I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important."
"They should have a desktop version to work on the platform."
"Operability with R could be improved."
"The data preparation capabilities need to be improved."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
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 54 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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