Dataiku Data Science Studio vs Microsoft Azure Machine Learning Studio comparison

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Executive Summary

We performed a comparison between Dataiku Data Science Studio and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: March 2024).
765,386 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"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.""Cloud-based process run helps in not keeping the systems on while processes are running.""I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.""Data Science Studio's data science model is very useful.""The solution is quite stable.""The most valuable feature is the set of visual data preparation tools.""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."

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"ML Studio is very easy to maintain.""It's a great option if you are fairly new and don't want to write too much code.""The solution is very fast and simple for a data science solution.""Their support is helpful.""Microsoft Azure Machine Learning Studio is easy to use and deploy.""The product supports open-source tools.""The solution is very easy to use, so far as our data scientists are concerned.""Visualisation, and the possibility of sharing functions are key features."

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Cons
"The ability to have charts right from the explorer would be an improvement.""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).""Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.""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 think it would help if Data Science Studio added some more features and improved the data model.""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.""I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

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"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.""A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer.""It would be great if the solution integrated Microsoft Copilot, its AI helper.""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.""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.""Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners.""Technical support could improve their turnaround time.""Microsoft should also include more examples and tutorials for using this product.​"

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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."
  • More Dataiku Data Science Studio Pricing and Cost Advice →

  • "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."
  • "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."
  • "From a developer's perspective, I find the price of this solution high."
  • "The licensing cost is very cheap. It's less than $50 a month."
  • "There is a license required for this solution."
  • "I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
  • "In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
  • "My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
  • More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Data Science Studio's data science model is very useful.
    Top Answer:I think it would help if Data Science Studio added some more features and improved the data model.
    Top Answer:The use case is data science, and we've deployed Data Science Studio in multiple regions for four environments: dev, preset, pre-production, and production.
    Top Answer:Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Ranking
    6th
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    9,728
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    7,542
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    190
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    10.0
    2nd
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    15,820
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    12,975
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    23
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    494
    Rating
    7.6
    Comparisons
    Also Known As
    Dataiku DSS
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Dataiku Data Science Studio 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.

    Dataiku Data Science Studio is also known as Dataiku DSS. This solution enables you to discover, share, and reuse code and applications so that you can deliver high-quality projects easily and streamline your path to production. As an enterprise leader, you can leverage the power of AI to confidently make business decisions.

    With Dataiku, an intuitive interface is guaranteed and allows users the ability to access and work with data using a point-and-click method. Dataiku analyzes the data to suggest key transformations. Beyond offering 109 data transformation capabilities, Dataiku also includes pipelines that can be generated in SQL which can thereafter be scheduled for automated recomputation.

    What's more, Dataiku allows you to create more than 20 different kinds of charts and also gives you the ability to deploy them into dashboards or create custom web applications for the use of interactive and sophisticated visualization tools.

    In addition, with Dataiku you have the option of using an in-depth statistical analysis, including but not limited to: curves fitting, univariate and bivariate analysis, principal component analysis, correlation analysis, and statistical tests.

    Dataiku Data Science Studio Consists Of:

    • Data preparation
    • Visualization
    • Machine Learning
    • Data Ops
    • ML Ops
    • Analytic Apps

    With Dataiku Data Science Studio You Can:

    • Integrate any data 10x faster
    • Build and automate sophisticated data pipelines
    • Build and share insights in minutes
    • Perform in-depth statistical analysis
    • Create thousands of models to find the best ones
    • Explore and explain models

    Dataiku Data Science Studio Benefits and Features:

    • Use your favorite languages and tools: You can create code working with tools you are already familiar with in the language you prefer (Python, R, SQL, etc.)
    • Easily reuse and share code: This feature helps you reduce inefficiencies and inconsistent data. Dataiku includes project libraries, allowing teams to centralize and share code. Although it comes pre-loaded with starter code for tasks, it also provides you with the ability to add your own code snippets.
    • Simplify complexities related to connecting to data and configuring computer resources: With this feature, data scientists can execute code in both a containerized and distributed way, while also selecting the runtime environment they want. Dataiku works to maintain those containers as well as shut them down when the job is completed.

    Features Users Find Most Valuable:

    • API
    • Reporting/Analytics
    • Third-Party Integrations
    • Data Import/Export
    • Natural Language Processing
    • Search/Filter
    • Monitoring
    • Workflow Management

    Reviews from Real Users

    PeerSpot users note that Dataiku Data Science Studio has a fantastic interface and is also flexible, intuitive, and stable. One user said "I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person." Another user mentioned “The best feature is the user interface. It allows us to see the visual flows.”

    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.

    Microsoft Azure Machine Learning Will Help You:

    • Rapidly build and train models
    • Operationalize at scale
    • Deliver responsible solutions
    • Innovate on a more secure hybrid platform

    With Microsoft Azure Machine Learning You Can:

    • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
    • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
    • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
    • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

    Microsoft Azure Machine Learning Features:

    • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
    • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
    • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
    • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

    Microsoft Azure Machine Learning Benefits:

    • It is fully integrated with Python and R SDKs.
    • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
    • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
    • It supports multiple model formats depending upon the job type.
    • It has automated model training and hyperparameter tuning with code-first and no-code options.
    • It supports data labeling projects.

    Reviews from Real Users:

    "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.” - Channing S.l, Owner at Channing Stowell Associates

    "The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

    "The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

    "The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

    Sample Customers
    BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm18%
    Educational Organization13%
    Manufacturing Company8%
    Computer Software Company8%
    REVIEWERS
    Financial Services Firm17%
    Energy/Utilities Company13%
    Comms Service Provider9%
    Retailer9%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    Computer Software Company10%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise19%
    Large Enterprise68%
    REVIEWERS
    Small Business30%
    Midsize Enterprise11%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Data Science Platforms
    March 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Dataiku Data Science Studio is ranked 6th in Data Science Platforms with 6 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 47 reviews. Dataiku Data Science Studio is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Dataiku Data Science Studio writes "The model is very useful". 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 Databricks, Alteryx, KNIME, RapidMiner and Amazon SageMaker, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and KNIME.

    See our list of best Data Science Platforms vendors.

    We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.