Compare Amazon SageMaker vs. IBM Watson Studio

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Most Helpful Review
Find out what your peers are saying about Amazon SageMaker vs. IBM Watson Studio and other solutions. Updated: September 2020.
442,845 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 few projects we have done have been promising.""They are doing a good job of evolving.""The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases.""Allows you to create API endpoints.""The deployment is very good, where you only need to press a few buttons."

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"The solution is very easy to use.""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.""The system's ability to take a look at data, segment it and then use that data very differently.""IBM Watson Studio consistently automates across channels.""It has a lot of data connectors, which is extremely helpful."

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Cons
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.""I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox.""AI is a new area and AWS needs to have an internship training program available.""Lacking in some machine learning pipelines.""Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

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"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier.""So a better user interface could be very helpful""The decision making in their decision making feature is less good than other options.""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.""Some of the solutions are really good solutions but they can be a little too costly for many.""The initial setup was complex."

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Pricing and Cost Advice
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation.""The support costs are 10% of the Amazon fees and it comes by default."

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Questions from the Community
Top Answer: Allows you to create API endpoints.
Top Answer: The pricing for the Notebook endpoints is a bit high, but generally reasonable.
Top Answer: The product has come a long way and they've added a lot of things, but in terms of improvement I would like to probably have features such as MLflow embedded into it. Additional features I would like… more »
Top Answer: It has a lot of data connectors, which is extremely helpful.
Top Answer: It will come down again to cost. Some of the solutions are really good solutions but they can be a little too costly for many. I think a lot of software vendors have considered having special pricing… more »
Top Answer: The initial setup was complex.
Ranking
14th
Views
10,392
Comparisons
9,138
Reviews
4
Average Words per Review
510
Avg. Rating
7.5
9th
Views
5,183
Comparisons
4,146
Reviews
5
Average Words per Review
468
Avg. Rating
8.2
Popular Comparisons
Compared 29% of the time.
Compared 5% of the time.
Compared 5% of the time.
Also Known As
AWS SageMaker, SageMakerWatson Studio, IBM Data Science Experience, Data Science Experience, DSx
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Amazon
IBM
Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

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.

Offer
Learn more about Amazon SageMaker
Learn more about IBM Watson Studio
Sample Customers
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, IntuitGroupM, Accenture, Fifth Third Bank
Top Industries
VISITORS READING REVIEWS
Computer Software Company32%
Media Company20%
Comms Service Provider9%
Insurance Company5%
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider15%
Retailer6%
Financial Services Firm5%
Company Size
REVIEWERS
Midsize Enterprise57%
Large Enterprise43%
REVIEWERS
Small Business78%
Large Enterprise22%
Find out what your peers are saying about Amazon SageMaker vs. IBM Watson Studio and other solutions. Updated: September 2020.
442,845 professionals have used our research since 2012.
Amazon SageMaker is ranked 14th in Data Science Platforms with 5 reviews while IBM Watson Studio is ranked 9th in Data Science Platforms with 6 reviews. Amazon SageMaker is rated 7.6, while IBM Watson Studio is rated 8.2. The top reviewer of Amazon SageMaker writes "A solution with great computational storage, has many pre-built models, is stable, and has good support". On the other hand, the top reviewer of IBM Watson Studio writes "Machine learning that can be applicable for other data sets without having to carry out the process all over again". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, Domino Data Science Platform and Cloudera Data Science Workbench, whereas IBM Watson Studio is most compared with Microsoft Azure Machine Learning Studio, IBM SPSS Modeler, Dataiku Data Science Studio, Alteryx and H2O.ai. See our Amazon SageMaker vs. IBM Watson Studio report.

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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.