H2O.ai

H2O AI Cloud

7.3 /10

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

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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.

81 Likeliness to Recommend

1
Since last award

95 Plan to Renew

2
Since last award

87 Satisfaction of Cost Relative to Value

1
Since last award

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Emotional Footprint Overview

+86 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

7% Neutral

93% Positive

Pros

Helps Innovate
Continually Improving Product
Inspires Innovation
Efficient Service

Feature Ratings

Average 80

Data Ingestion

Pre-Packaged AI/ML Services

Algorithm Diversity

Performance and Scalability

Data Exploration and Visualization

Feature Engineering

Openness and Flexibility

Algorithm Recommendation

Model Tuning

Data Pre-Processing

Model Monitoring and Management

Vendor Capability Ratings

Average 80

Breadth of Features

Product Strategy and Rate of Improvement

Ease of Implementation

Ease of Customization

Quality of Features

Business Value Created

Ease of Data Integration

Ease of IT Administration

Vendor Support

Usability and Intuitiveness

Availability and Quality of Training

H2O AI Cloud Reviews

priyank s.

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

Validated Review

Verified Reviewer

Submitted Nov 2022

Outstanding framework and application

Likeliness to Recommend

8 /10

What differentiates H2O AI Cloud from other similar products?

I like how it includes a graphical user interface for estimating how long the model will run, as well as a water meter that shows how the resources are used during the modeling process.

What is your favorite aspect of this product?

H2O provides a well-validated, fully automated, rigorous machine-learning pipeline that includes state-of-the-art model interpretation for prediction and inference.

What do you dislike most about this product?

H20 does a decent job of abstracting the underlying transformation and tuning stages, which may be tough. Better instructions and better tutorials would be beneficial.

What recommendations would you give to someone considering this product?

This is a cross-platform, integrated ML framework that is always evolving with issues relevant to data scientists. The best feature of H2O.ai is its simplicity and smooth user interface.

Pros

Continually Improving Product
Reliable
Trustworthy
Unique Features

Praveen P.

Role: Sales Marketing
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Nov 2022

H2O.ai is a fantastic tool for ML

Likeliness to Recommend

8 /10

What differentiates H2O AI Cloud from other similar products?

We are developing Predictive Maintenance and Analytics, which might be utilized in Manufacturing and factories. Streamlining our machine learning model generation and deployment process. Advertising technology modeling Can build complex models from data sets with billions of rows. Significantly faster turnaround time

What is your favorite aspect of this product?

Excellent integration of development and production. A powerful and simple visual interface to a complex backend. Rapid product iteration and continuous improvement

What do you dislike most about this product?

In comparison to python pandas or pyspark dataframes, H2O Frames have very limited data processing options. I believe that having more data maneuverability would be extremely beneficial.

What recommendations would you give to someone considering this product?

Gartner Magic Quadrant brought me to h2o.ai. It's an excellent ML platform with a large built-in library. To score our client database, we tested churn prediction systems.

Pros

Helps Innovate
Enables Productivity
Trustworthy
Efficient Service

Role: Information Technology
Industry: Technology
Involvement: End User of Application

Validated Review

Anonymous Reviewer

Submitted Nov 2022

A really positive Machine Learning experience

Likeliness to Recommend

8 /10

Pros

Continually Improving Product
Performance Enhancing
Enables Productivity
Trustworthy

H2O AI Cloud

What is H2O AI Cloud?

Company Details

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Awards & Recognition

H2O AI Cloud Ratings

81 Likeliness to Recommend

1 Since last award

95 Plan to Renew

2 Since last award

87 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+86 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.

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

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

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.

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

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

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

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

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

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 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.

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.

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.

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.

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

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.

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.

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

priyank s.

Outstanding framework and application

Likeliness to Recommend

Pros

Praveen P.

H2O.ai is a fantastic tool for ML

Likeliness to Recommend

Pros

A really positive Machine Learning experience

Likeliness to Recommend

Pros

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Feature Ratings

Data Ingestion

Pre-Packaged AI/ML Services

Algorithm Diversity

Performance and Scalability

Data Exploration and Visualization

Feature Engineering

Openness and Flexibility

Algorithm Recommendation

Model Tuning

Data Pre-Processing

Model Monitoring and Management

Vendor Capability Ratings

Breadth of Features

Product Strategy and Rate of Improvement

Ease of Implementation

Ease of Customization

Quality of Features

Business Value Created

Ease of Data Integration

Ease of IT Administration

Vendor Support

Usability and Intuitiveness

Availability and Quality of Training