Compare Dataiku Data Science Studio vs. Microsoft Azure Machine Learning Studio
Dataiku Data Science Studio is ranked 12th in Data Science Platforms with 4 reviews while Microsoft Azure Machine Learning Studio is ranked 6th in Data Science Platforms with 8 reviews. Dataiku Data Science Studio is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.2. The top reviewer of Dataiku Data Science Studio writes "User interface is colorful, beautiful, and well-designed but sometimes the solution can be slow". 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 Data Science Studio is most compared with Alteryx, KNIME and Databricks, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx and Amazon SageMaker. See our Dataiku Data Science Studio vs. Microsoft Azure Machine Learning Studio report.
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Most Helpful Review | |||
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 Find out what your peers are saying about Dataiku Data Science Studio vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020. 390,232 professionals have used our research since 2012. | |||
Quotes From Members
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Pros | |
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. 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. Cloud-based process run helps in not keeping the systems on while processes are running. | The most valuable feature is data normalization. The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses. The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure. Visualisation, and the possibility of sharing functions are key features. 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. When you import the dataset you can see the data distribution easily with graphics and statistical measures. Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most. 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. |
Cons | |
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. The ability to have charts right from the explorer would be an improvement. Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable. Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days). 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. | The data cleaning functionality is something that could be better and needs to be improved. Integration with social media would be a valuable enhancement. If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice. Operability with R could be improved. I would like to see modules to handle Deep Learning frameworks. I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated. Enable creating ensemble models easier, adding more machine learning algorithms. It could use to add some more features in data transformation, time series and the text analytics section. |
Pricing and Cost Advice | |
The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything. | From a developer's perspective, I find the price of this solution high. When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly. To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS. |
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Ranking  | |
Views 7,866 Comparisons 5,723 Reviews 4 Average Words per Review 493 Avg. Rating 7.5 | Views 9,703 Comparisons 8,066 Reviews 8 Average Words per Review 479 Avg. Rating 7.3 |
Top Comparisons | |
 Compared 36% of the time.  Compared 14% of the time.  Compared 10% of the time. | Compared 22% of the time. Compared 11% of the time.  Compared 11% of the time. |
Also Known As | |
| Dataiku DSS | Azure Machine Learning |
Learn | |
| Dataiku | Microsoft |
Overview | |
Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. | Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models. |
Offer | |
Learn more about Dataiku Data Science Studio | Learn more about Microsoft Azure Machine Learning Studio |
Sample Customers | |
| BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto | Information Not Available |
Top Industries | |
Software R&D Company30% Financial Services Firm16% Comms Service Provider7% Manufacturing Company5% | Software R&D Company31% Comms Service Provider18% Manufacturing Company6% Media Company5% |
Find out what your peers are saying about Dataiku Data Science Studio vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
390,232 professionals have used our research since 2012.
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