Compare IBM Watson Studio vs. Microsoft Azure Machine Learning Studio

IBM Watson Studio is ranked 9th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 6th in Data Science Platforms with 8 reviews. IBM Watson Studio is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.2. The top reviewer of IBM Watson Studio writes "It has greatly improved the performance because it is standardized across the company". 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 IBM SPSS Modeler, Microsoft Azure Machine Learning Studio and Amazon SageMaker, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx and Amazon SageMaker. See our IBM Watson Studio vs. Microsoft Azure Machine Learning Studio report.
Cancel
You must select at least 2 products to compare!
Most Helpful Review
Find out what your peers are saying about IBM Watson Studio vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
389,722 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
The scalability of IBM Watson Studio is great.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 solution is very easy to use.It is a stable, reliable product.Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic.The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people.It has greatly improved the performance because it is standardized across the company.

Read more »

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.

Read more »

Cons
The decision making in their decision making feature is less good than other options.So a better user interface could be very helpfulMore features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier.We would like to see it more web-based with more functionality.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.Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge.

Read more »

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.

Read more »

Pricing and Cost Advice
Information Not Available
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.

Read more »

report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
389,722 professionals have used our research since 2012.
Ranking
9th
Views
4,270
Comparisons
3,271
Reviews
5
Average Words per Review
456
Avg. Rating
8.2
6th
Views
9,703
Comparisons
8,066
Reviews
8
Average Words per Review
479
Avg. Rating
7.3
Top Comparisons
Also Known As
Watson Studio, IBM Data Science Experience, Data Science Experience, DSxAzure Machine Learning
Learn
IBM
Microsoft
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.

Offer
Learn more about IBM Watson Studio
Learn more about Microsoft Azure Machine Learning Studio
Sample Customers
GroupM, Accenture, Fifth Third Bank
Information Not Available
Top Industries
VISITORS READING REVIEWS
Software R&D Company39%
Retailer11%
Comms Service Provider8%
Media Company5%
VISITORS READING REVIEWS
Software R&D Company31%
Comms Service Provider18%
Manufacturing Company6%
Media Company5%
Find out what your peers are saying about IBM Watson Studio vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
389,722 professionals have used our research since 2012.
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.