H2O

H2O

Score 8.4 out of 10
H2O

Overview

What is H2O?

H2O.ai is an open-source predictive analytics and machine learning platform.
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Recent Reviews

H2O AutoML superb!!

8 out of 10
September 18, 2019
We use H2O.ai for building End to End auto pipelines for machine learning models. It has massively good support with big data. For that we …
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Pricing

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What is H2O?

H2O.ai is an open-source predictive analytics and machine learning platform.

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

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Product Demos

H2O Driverless AI Demo
06:02
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Product Details

What is H2O?

H2O.ai is an open-source predictive analytics and machine learning platform.

H2O Video

Watch this end-to-end demo of H2O Driverless AI. This demo includes: (1) Data Visualization (2) An AI experiment (3) Machine Learning Interpretability (4) One-click deployment (5) Bring Your Own Recipe This demo gives you the perfect overview in just over 6 minutes!

H2O Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

 (11)

Attribute Ratings

Reviews

(1-2 of 2)
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September 18, 2019

H2O AutoML superb!!

Score 8 out of 10
Vetted Review
Verified User
We use H2O.ai for building End to End auto pipelines for machine learning models. It has massively good support with big data. For that we use H2O's Sparkling Water. As far as I have experienced, H2O gives the highest accuracy among all other autoML tools. I have used it in our one of the projects and I had to deliver in just 1 week. Building an ML model with H2O, as well as fast training and auto tuning, helped me a lot.
  • AutoML
  • Bigdata support with H2O's Sparkling Water
  • more state of the art algorithm can be added
  • Containerization facilities like Docker should be given
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use.
If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
Platform Connectivity (2)
80%
8.0
Connect to Multiple Data Sources
80%
8.0
Automatic Data Format Detection
80%
8.0
Data Exploration (2)
85%
8.5
Visualization
80%
8.0
Interactive Data Analysis
90%
9.0
Data Preparation (3)
93.33333333333334%
9.3
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
90%
9.0
Built-in Processors
90%
9.0
Platform Data Modeling (4)
100%
10.0
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
90%
9.0
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
80%
8.0
I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Score 10 out of 10
Vetted Review
Verified User
H2O is used as a core tool across the whole organization. The primary business we are in is measuring the Return on Ad Spend (ROAS) for advertisers, media companies and CPG marketing and product companies.
  • Flexible modeling including Ensemble
  • Open Source - so that we can know what is really happening and can request changes when needed
  • Ability to scale up horizontally by provisioning dynamic clusters
  • Access to core development team and speed of problem resolution and feature additions
  • Better documentation
  • Improve the Visual presentations including charting etc
It is able to handle large amounts of data. It is best suited when we want to productionalize BI and Analytical applications/features with ease and scale well. Applicable for ensemble learning, data munging, scaled application development.

Not yet ready for fast, quick and dirty prototyping.
  • Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
  • Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
  • Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
H2O provided all the needed features such as Linear Modeling, Targeted Learning, Predictive Analytics including GLM, Trees, Neural networks and ensemble with ease. We are also able to pick and choose what we want without deploying all the bulky tools unlike others. Able to package H2O jar with our home grown code for remote deployments without worrying able expensive licenses.
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