H2O.ai

H2O AI Cloud

Composite Score

7.3 /10

CX Score

7.6 /10

14 Reviews Write a Review

Claim Software

What is H2O AI Cloud?

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O 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.

Company Details

Need Assistance?

We're here to help you with understanding our reports and the data inside to help you make decisions.

Get Assistance

Awards & Recognition

H2O AI Cloud won the following awards in the Machine Learning Platforms category

Filter By

5 Top Rated Capabilities

3 Top Rated Features

2024

4 Top Rated Capabilities

3 Top Rated Features

2023

H2O AI Cloud Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on H2O AI Cloud.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

80 Likeliness to Recommend

1
Since last award

96 Plan to Renew

1
Since last award

86 Satisfaction of Cost Relative to Value

2
Since last award

{y}

{name}

Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+87 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love H2O AI Cloud?

0% Negative

6% Neutral

94% Positive

Pros

Helps Innovate
Continually Improving Product
Inspires Innovation
Efficient Service

Feature Ratings

Average 79

Data Ingestion

Pre-Packaged AI/ML Services

Data Exploration and Visualization

Feature Engineering

Algorithm Recommendation

Performance and Scalability

Algorithm Diversity

Openness and Flexibility

Model Tuning

Model Monitoring and Management

Ensembling

Vendor Capability Ratings

Average 81

Breadth of Features

Product Strategy and Rate of Improvement

Ease of Customization

Ease of Implementation

Business Value Created

Quality of Features

Ease of Data Integration

Usability and Intuitiveness

Ease of IT Administration

Vendor Support

Availability and Quality of Training

H2O AI Cloud Reviews

Piyush M.

Role: Consultant
Industry: Consulting
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Nov 2024

Fantastic product

Likeliness to Recommend

8 /10

What differentiates H2O AI Cloud from other similar products?

Ai featured

What is your favorite aspect of this product?

Algorithm

What do you dislike most about this product?

Not applicable

What recommendations would you give to someone considering this product?

Amazing product

Pros

Helps Innovate
Reliable
Performance Enhancing
Effective Service

Pulkit J.

Role: Consultant
Industry: Consulting
Involvement: Initial Implementation

Validated Review

Verified Reviewer

Submitted May 2024

Good product to implement ML soutions

Likeliness to Recommend

7 /10

What differentiates H2O AI Cloud from other similar products?

AutoML functionality

What is your favorite aspect of this product?

AutoML with minimal code

What do you dislike most about this product?

Easy to implement without good coding knowledge

What recommendations would you give to someone considering this product?

If you are short of programmers, this is a good option

Pros

Trustworthy
Fair
Client's Interest First
Transparent

Ashay S.

Role: Information Technology
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Jan 2023

Best Auto-ML platform

Likeliness to Recommend

10 /10

What differentiates H2O AI Cloud from other similar products?

One of the great things about it apart from the model building and deployment part is it also provides some tool access for better Interpretability and Explainability of models. It is helpful in understanding how the model works and what were the important features than contributed to getting that performance.

What is your favorite aspect of this product?

It is very fast and easy to try out multiple models on a given dataset and find the best one. It helps in hyper-parameter optimization to find best performance out of a given model , this can be monitored on the dashboard as the process is going so we needn't wait for the whole process to complete to make changes or tune it. Also the performance of the available algorithms is quite good.

What do you dislike most about this product?

There are some parts which lack good documentation and for some specific cases it lacks the algorithm support or some of the data processing steps. But unless it is a very complex case , the performance is quite good for a given task

What recommendations would you give to someone considering this product?

It has a great set of ML algorithms and support for various stages of ML pipelines that can save your time and reduce chances of error. It is quite intuitive and user friendly and doesn't require someone to be an expert in ML to use.

Pros

Helps Innovate
Continually Improving Product
Enables Productivity
Efficient Service

H2O AI Cloud

What is H2O AI Cloud?

Company Details

Need Assistance?

Awards & Recognition

H2O AI Cloud Ratings

80 Likeliness to Recommend

1 Since last award

96 Plan to Renew

1 Since last award

86 Satisfaction of Cost Relative to Value

2 Since last award

Emotional Footprint Overview

+87 Net Emotional Footprint

How much do users love H2O AI Cloud?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Feature Engineering Ability to extract and build features from raw data.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Model Tuning 'Ability to fine-tune models based on monitoring feedback within the platform.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Ensembling 'Ability to combine ML models for best results.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

H2O AI Cloud Reviews

Piyush M.

Fantastic product

Likeliness to Recommend

Pros

Pulkit J.

Good product to implement ML soutions

Likeliness to Recommend

Pros

Ashay S.

Best Auto-ML platform

Likeliness to Recommend

Pros

Most Popular H2O AI Cloud Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

AWS Machine Learning

Microsoft Azure Machine Learning

DataRobot AI Platform

Databricks Data Intelligence Platform

1
Since last award

1
Since last award

2
Since last award

Feature Ratings

Data Ingestion

Pre-Packaged AI/ML Services

Data Exploration and Visualization

Feature Engineering

Algorithm Recommendation

Performance and Scalability

Algorithm Diversity

Openness and Flexibility

Model Tuning

Model Monitoring and Management

Ensembling

Vendor Capability Ratings

Breadth of Features

Product Strategy and Rate of Improvement

Ease of Customization

Ease of Implementation

Business Value Created

Quality of Features

Ease of Data Integration

Usability and Intuitiveness

Ease of IT Administration

Vendor Support

Availability and Quality of Training