Dataiku vs Microsoft Azure Machine Learning Studio comparison

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Dataiku Logo
8,856 views|6,927 comparisons
100% willing to recommend
Microsoft Logo
13,354 views|10,922 comparisons
92% willing to recommend
Comparison Buyer's Guide
Executive Summary

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.
To learn more, read our detailed Dataiku vs. Microsoft Azure Machine Learning Studio Report (Updated: May 2024).
787,033 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 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."

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"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."

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Cons
"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."

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"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."

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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."
  • "Pricing is pretty steep. Dataiku is also not that cheap."
  • More Dataiku 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:Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend towards a tool.
    Top Answer:The no-code/low-code aspect, where DataRobot doesn't need much coding at all. Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more… more »
    Top Answer:My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataiku as a Jupyter Notebook. We use it a lot for marketing and analytics. The marketing… more »
    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 »
    Top Answer:The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
    Top Answer:I would rate the costliness of the solution as a nine out of ten.
    Ranking
    7th
    Views
    8,856
    Comparisons
    6,927
    Reviews
    1
    Average Words per Review
    525
    Rating
    8.0
    2nd
    Views
    13,354
    Comparisons
    10,922
    Reviews
    28
    Average Words per Review
    515
    Rating
    7.8
    Comparisons
    Also Known As
    Dataiku DSS
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

    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 Organization14%
    Manufacturing Company8%
    Computer Software Company8%
    REVIEWERS
    Financial Services Firm19%
    Energy/Utilities Company12%
    Computer Software Company8%
    Manufacturing Company8%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    Computer Software Company10%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business57%
    Large Enterprise43%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise19%
    Large Enterprise68%
    REVIEWERS
    Small Business37%
    Midsize Enterprise10%
    Large Enterprise54%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Dataiku vs. Microsoft Azure Machine Learning Studio
    May 2024
    Find out what your peers are saying about Dataiku vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: May 2024.
    787,033 professionals have used our research since 2012.

    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.

    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.