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."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."
"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."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The most valuable feature is the set of visual data preparation tools."
"The solution is quite stable."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The interface is very intuitive."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"It has helped in reducing the time involved for coding using R and/or Python."
"MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
"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."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"The ability to have charts right from the explorer would be an improvement."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"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."
"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."
"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."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"Microsoft should also include more examples and tutorials for using this product."
"The interface is a bit overloaded."
"Operability with R could be improved."
"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."
"They should have a desktop version to work on the platform."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
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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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