Compare Amazon SageMaker vs. H2O.ai

Amazon SageMaker is ranked 13th in Data Science Platforms with 2 reviews while H2O.ai is ranked 10th in Data Science Platforms with 7 reviews. Amazon SageMaker is rated 7.6, while H2O.ai is rated 7.6. 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 H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio and Domino Data Science Platform, whereas H2O.ai is most compared with KNIME, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio. See our Amazon SageMaker vs. H2O.ai report.
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Amazon SageMaker Logo
5,586 views|5,163 comparisons
H2O.ai Logo
6,013 views|4,112 comparisons
Most Helpful Review
Find out what your peers are saying about Amazon SageMaker vs. H2O.ai and other solutions. Updated: January 2020.
389,978 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
They are doing a good job of evolving.The few projects we have done have been promising.

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The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people.One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm.The ease of use in connecting to our cluster machines.It is helpful, intuitive, and easy to use. The learning curve is not too steep.AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O.

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Cons
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.I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.

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On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time.The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.I would like to see more features related to deployment.The model management features could be improved.It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O.

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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.

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We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.

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Ranking
13th
Views
5,586
Comparisons
5,163
Reviews
2
Average Words per Review
517
Avg. Rating
7.5
10th
Views
6,013
Comparisons
4,112
Reviews
7
Average Words per Review
320
Avg. Rating
7.6
Top Comparisons
Compared 30% of the time.
Compared 24% of the time.
Also Known As
AWS SageMaker, SageMaker
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Amazon
H2O.ai
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.

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

Offer
Learn more about Amazon SageMaker
Learn more about H2O.ai
Sample Customers
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuitpoder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Top Industries
VISITORS READING REVIEWS
Software R&D Company34%
Media Company15%
Comms Service Provider9%
Retailer8%
VISITORS READING REVIEWS
Software R&D Company41%
Comms Service Provider11%
Transportation Company10%
Manufacturing Company9%
Find out what your peers are saying about Amazon SageMaker vs. H2O.ai and other solutions. Updated: January 2020.
389,978 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.