IBM Watson Studio vs Microsoft Azure Machine Learning Studio comparison

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Executive Summary

We performed a comparison between IBM Watson Studio 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 IBM Watson Studio vs. Microsoft Azure Machine Learning Studio Report (Updated: March 2024).
765,234 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
"It is a stable, reliable product.""It has a lot of data connectors, which is extremely helpful.""It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements.""Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic.""The solution is very easy to use.""For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks.""The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video.""The scalability of IBM Watson Studio is great."

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"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.""The product's standout feature is a robust multi-file network with limited availability.""The most valuable feature of the solution is the availability of ChatGPT in the solution.""The solution is very fast and simple for a data science solution.""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.""What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it.""The solution is scalable.""The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."

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Cons
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers.""We would like to see it more web-based with more functionality.""Watson Studio would be improved with a clearer path for the deployment of docker images.""I think maybe the support is an area where it lacks.""More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier.""I want IBM's technical support team to provide more specific answers to queries.""Some of the solutions are really good solutions but they can be a little too costly for many.""It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."

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"The price could be improved.""​It could use to add some more features in data transformation, time series and the text analytics section.""The product must improve its documentation.""Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.""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.""The regulatory requirements of the product need improvement.""Enable creating ensemble models easier, adding more machine learning algorithms."

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Pricing and Cost Advice
  • "Watson Studio's pricing is reasonable for what you get."
  • "IBM Watson Studio is a reasonably priced product"
  • "IBM Watson Studio is an expensive solution."
  • "The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."
  • More IBM Watson 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:From an improvement perspective, I would say that if the deployment environment and IBM Watson Studio's environment are separated, then it would be good. I would like them to be one and offer users a… 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 »
    Ranking
    9th
    Views
    3,554
    Comparisons
    2,387
    Reviews
    5
    Average Words per Review
    426
    Rating
    8.2
    2nd
    Views
    15,820
    Comparisons
    12,975
    Reviews
    23
    Average Words per Review
    494
    Rating
    7.6
    Comparisons
    Also Known As
    Watson Studio, IBM Data Science Experience, Data Science Experience, DSx
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    IBM
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    Overview

    IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

    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
    GroupM, Accenture, Fifth Third Bank
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    REVIEWERS
    Manufacturing Company22%
    Marketing Services Firm11%
    Comms Service Provider11%
    Retailer11%
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Computer Software Company12%
    Educational Organization8%
    Comms Service Provider7%
    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
    REVIEWERS
    Small Business71%
    Large Enterprise29%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise13%
    Large Enterprise66%
    REVIEWERS
    Small Business30%
    Midsize Enterprise11%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise72%
    Buyer's Guide
    IBM Watson Studio vs. Microsoft Azure Machine Learning Studio
    March 2024
    Find out what your peers are saying about IBM Watson Studio vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    IBM Watson Studio is ranked 9th in Data Science Platforms with 13 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 47 reviews. IBM Watson Studio is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". 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". IBM Watson Studio is most compared with Databricks, Azure OpenAI, Google Vertex AI, Amazon Comprehend and IBM SPSS Modeler, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and IBM SPSS Statistics. See our IBM Watson Studio vs. Microsoft Azure Machine Learning Studio report.

    See our list of best Data Science Platforms vendors and best AI Development 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.