DATASHEET
2021

1DATA SHEET 2021 - H2O.ai

Key Features of
The H2O Hybrid Cloud
The H2O AI Hybrid Cloud solves complex business problems and accelerates the discovery of
new ideas with results you can understand and trust. Our comprehensive automated machinelearning (autoML) capabilities transform how AI is created and consumed. We have built AI to do
AI, making it easier and faster to use, while still maintaining expert levels of accuracy, speed andtransparency.

We deliver on our mission to democratize AI by moving people from idea to impact with theconfidence to make, operate and innovate with AI on the H2O AI Hybrid Cloud.

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Agility and transparency around the creation and use of AI solutions ensure acycle of continuous learning and innovation. Enhanced cross-team collaboration
improves the overall quality of results, as well as the effectiveness of responses
to evolving circumstances and new insights.

Data Scientists
Advanced autoML capabilities encompass theentire data science lifecycle, optimizing workflows
to increase both the quantity and quality ofdata science projects delivered to your businessstakeholders. Robust features for machine learninginterpretability and operations give you seamless
control over the entire explainability pipeline,
resulting in higher accuracy, increased transparency
and faster adaptations to changing circumstances.

Developers
Automatic conversion of deployment artifacts
from Python/R to Java/C++ simplifies the handoff
of projects between data science teams anddevelopers, and provides seamless integration of
AI capabilities into existing solutions. Our opensource, low code development framework provides
flexibility to work with all major libraries andframeworks and accelerates the creation of new
AI applications.

Machine Learning Engineers
Guarantee that deployed models are operating as
intended with comprehensive model monitoring
features designed to support both H2O.ai andthird party models. The H2O AI Hybrid Cloud’s
comprehensive model management capabilities areaccessible through a visual interface or API.

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DevOps and IT Professionals
The H2O AI Hybrid Cloud provides high performancecomputing with full NVIDIA RAPIDS integration.
Easily scale workloads with support for theunprecedented compute and network accelerationof Ampere-based NVIDIA GPUs and latest CUDA
runtime. Monitor system usage and provide metrics
for resource monitoring and autoscaling of multinode clusters through our platform performance API.

Business Users
The AI AppStore makes it easy and intuitive forbusiness users to find and access AI applications
custom built to meet your unique business needs.
These solutions are powered by robust machinelearning models and provide real-time insights fordata driven decisions.

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Make.
H2O.ai built AI to do AI. With industry leading automatedmachine learning (autoML), the H2O AI Hybrid Cloud gives users
more accuracy, speed and transparency throughout the entire
machine learning lifecycle, including in the development anddeployment of AI applications.

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Feature Transformation
Automatically visualize and address
data quality issues with advanced
feature engineering that transforms
your data into an optimal modelingdataset.

Data Intelligence••

Data visualization Visually display
interesting statistical propertiesof your dataset and expose
unexpected data quality issues likeoutliers, correlations or missing
values.

•Pre-processing transformers
Automatically include custom datapreparation as part of your final
deployed machine learning pipeline.

•

Dataset splitting Save time andimprove validation with a varietyof built-in splitting techniques,
including splitting randomly, bytime, with stratification and withfull customization via live code.

Automatic data insights Visual andtext descriptions for automaticallydetected trends and insights
including topics in text, correlations,and outliers.

•

Missing value handling Produce
higher accuracy and better
generalization with end to end
support for missing values in allparts of the machine learning
pipeline.

•

Outlier detection Expose issues
or irregularities in data with better
accuracy delivered through various
proprietary algorithms.

•

Automated validation and crossvalidation Improve accuracy,
robustness and generalization
with a multitude of proprietaryvalidation techniques, statisticalmethods and moving windows.

Feature Engineering

6•

Automated feature engineering
Increase accuracy and ROI with ourproprietary feature engineering that
automatically extracts non-trivial
statistical information from your
data.

•

Feature encoding Convert mixed
data types (numeric, categorical,
text, image, date/time, etc.) in asingle dataset for use by machinelearning algorithms.

•

Feature transformation recipes
Apply your domain knowledge
to refine automated featureengineering outputs with fully
customizable Python recipes.

•

Per-Feature Controls Disablefeature engineering and feature
selection for certain columns
in your dataset, and pass themas-is to the model to satisfy yourcompliance requirements.

DATA SHEET 2021 - H2O.ai

Machine Learning
Quickly create and test highly accurate
and robust models with state-of-the-art
automated machine learning that spansthe entire data science lifecycle and canprocess a variety of data types within asingle dataset.

Automated Machine Learning (autoML) AutoML is pervasive across the entire H2O
AI Hybrid Cloud. Powering everything from feature transformation to model selection,
monitoring and deployment, robust autoML capabilities are the engine behind ourability to deliver AI that does AI.
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Automated feature selection(dimensionality reduction)
Reduce model complexity,
produce faster inference timeand better model interpretability
with a multitude of proprietaryfeature selection techniques that
automatically select the most
predictive features for your dataset.
Automatic Feature Engineering
Increase accuracy and ROI with
our proprietary grandmasterlevel feature engineering that
automatically extracts predictive
statistical information from yourdata for highest accuracy and forgaining actionable insights into thecausal nature of the data.
Hyperparameter Autotuning
Increase accuracy, ROI and timesavings with optimization acrossall components of the machinelearning modeling pipelinedelivered through a mix of our
proprietary, genetic algorithm,
Monte Carlo, Particle Swarm andBayesian methods.
Champion/Challenger model
selection Speed up testing andvalidation with autoML that finds
the best combination of featuresand models and automatic selectionof the best machine learning model
to fit your dataset.

•

Model ensembling Multiple
levels of both fully automatic andeasily customizable ensembling toincrease accuracy and ROI.

•

Automatic label assignment
Reduce error rates and save timewith automatic labeling thatpredicts the class for every scoredrecord, in addition to returning the
per-class probabilities.

•

Automated model documentation
Provide insight into the machinelearning process with automaticallygenerated documentation that
describes the experiment process,model tuning results, variable
importance, model importance,
model performance and detailed
settings for reproducibility.

•

Machine learning interpretability(MLI) AutoML powers a robust
MLI toolkit to include explanations,visualizations and customizations.

•

Model validation Assess model
robustness and mitigate risks in
production by obtaining a holistic
view of the models and preventing
failures on new data.

•

Unsupervised automatic machinelearning Immediately get new
insights on your unlabeled datawith unsupervised techniques suchas clustering to automatically group
topics, outlier detection to identify
irregularities in your data, anddimensionality reduction to reduce
model overfitting and complexity.

•

Wide dataset handling Handle
datasets with millions of columns
with GPU acceleration.

•

Imbalanced dataset handling
Improve the accuracy in imbalanced
use cases with access to special,
proprietary algorithms whichemphasize accuracy of rare classes
over the more frequent but lessvaluable classes.

•

Model leaderboards Speedup model development, model
selection and model validation with
automatic leaderboards comparing
best-of-breed autoML modelingapproaches.

DATA SHEET 2021 - H2O.ai

Time Series Forecasting Easily fit and solve forecasting problems with unique featureengineering and autoML capabilities specifically designed to handle time series data.
•

Diagnostics Automaticallyshow model performance acrossgroups and how predictions weregenerated with the ability to build
forecasts across many categories(individuals skus, product hierarchy,etc.).

•

Leaderboard for forecasting
Save time getting an optimized
forecasting model with a newleaderboard mode specific to timeseries experiments. Automaticallydesign and run multiple
experiments with varying amountsof pairs (train-test gap, forecasthorizon, etc.) to help with model
selection.

•

Time series machine learninginterpretability Time series models
work with various MLI explainers,
including Sensitivity Analysis,
Disparate Impact Analysis, Partial
Dependence/Individual Conditional
Expectation, Naïve and Kernel
Shapley, Surrogate Models and allfeature importance techniques.

Natural Language Processing (NLP) Extract insights from unstructured text data todiscover trends, create more accurate and relevant information retrieval and create
personalized recommendations.
•

Text preprocessing Remove noisefrom text and improve downstream
NLP tasks by creating a pipelineof preprocessing techniques likestop word removal, stemming/
lemmatization, emoji extraction and
more.

•

Text classification Tag andcategorize similar terms andconcepts with a range of supported
algorithms from simplistic TFIDF tostate-of-the-art BERT transformers.

•

Topic clustering Gain insights
for emerging trends and early

detection of rising problems
by automatically extracting
relationships in text, with theoption to fine tune clusters withhuman feedback to improve model
accuracy.
•

Named Entity Recognition (NER)
Categorize terms into common
groupings such as people, places,
organizations, dates, currency and
more.

•

Sentiment analysis Detect trends
in perspectives regarding specifictopics such as products and services
by identifying positive, negativeand neutral connotations.

•

NLP machine learninginterpretability Understandhow token importance varies
between classes with newly added
multinomial support. Understandthe impact of tokens on outcomeswith LOCO (leave one covariateout) 2.0 and Vectorizer + Linear
Model (VLM). Calculate theaverage outcome of a model whena text token is included versus notincluded with Partial Dependence
for Text Tokens. Select between
TF-IDF or the newly added
Vectorizer + Linear Model (VLM)
when it comes to generating tokens
for Surrogate models.

Computer Vision Use image data for modeling with the ability to combine additional datatypes with your image data to include text, tabular and audio data.
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Image processing Recognize
objects in images and categorize
them with high accuracy using all
the latest convolutional neuralnetworks (CNNs) out of the box
with GPU accelerated training.

••Video processing Input videos
are automatically transformed into
a set of frames, which are thenhandled using image processing
capabilities.
Audio processing Input audio data
is automatically transformed into

MEL-spectrograms, which are thenhandled using image processing
capabilities.
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Explanations for computer visionmodels Understand what part ofan image or video led to a specificprediction using techniques such as
Grad-CAM.

DATA SHEET 2021 - H2O.ai

Explainable AI
Easily understand the ‘why’ behind model
predictions to build better models andprovide explanations of model output at a
global level (across a set of predictions) or
at a local level (for an individual prediction).

White Box Models••

Generalized Linear Models(GLM) GLMs are an extension of
traditional linear models. They arehighly explainable models with theflexibility of the model structure
unifying the typical regression
methods (such as linear regression
and logistic regression for binaryclassification).
Generalized Additive Models(GAM) A GAM is a Generalized
Linear Model (GLM) in which
the linear predictor depends on
predictor variables and smoothfunctions of predictor variables.

•

Generalized Additive Models
with two-way interaction terms(GA2M) GA2M is an extensionof GAM which selects the mostimportant interactions between
features and includes functions of
those pairs of features in the model.

•

Explainable neural networks(XNN) These neural networks
consist of numerous subnetworks,
each of which learns aninterpretable function of theoriginal features.

•

Rulefit This algorithm fits a tree
ensemble to the data, builds a rule•

Shapley reason codes Provide
model explainability at a record
level for non-linear models for
global and individual records.

•

Surrogate Decision Trees Identify
the driving factors of a complex
model’s predictions in a very simple,
visual and straightforward way.

ensemble by traversing each tree,
evaluates the rules on the data tobuild a rule feature set, and fits asparse linear model (LASSO) to therule feature set joined with theoriginal feature set. Available in
Driverless AI by default.
•

Skopes Rules This algorithm learns
a simple set of rules for performingclassification.

•

Decision tree and linear model
combination This model segments
the data using a decision tree andthen fits a GLM to the data at eachleaf node.

•

Individual Conditional
Expectation (ICE) Plot that shows
how a column affects predictions at
an individual level, with the ability
to drill down to any row of choiceand compare/contrast with average
partial dependence.

•k-LIME reason codes Generate
novel reason codes at a record
level, subsets of the dataset or at
an aggregated level for the entire
dataset.

Interpretability Methods••

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Partial Dependence Plot (PDP)
Plot that shows how a column
affects predictions at a global level,with the ability for users to explore
columns and how they affectpredictions.
Feature importance Calculate
which features are important
for the model’s decision making,both naive and with transformedfeatures.

•Leave One Covariate Out (LOCO)
Identify features that are importantto the Surrogate Random Forest
predictions from an aggregated or
row level view.

DATA SHEET 2021 - H2O.ai

Bias Detection•

Disparate Impact Analysis Identify
areas in your data where a modelshows bias across various metrics
with a dashboard that shows
disparity between groups in thedataset.

•Sensitivity analysis Provide model
explanation at a very granular level
without much overhead with a toolthat allows you to score a trainedmodel on a single row, multiple
rows or an entire dataset andcompare the model’s new outcome
to the predicted outcome on theoriginal dataset.

•

Explanations for NLP models
Understand how token importancevaries between classes with
newly added multinomial support.
Understand the impact of tokens
on outcomes with LOCO (leave one
covariate out) 2.0 and Vectorizer +Linear Model (VLM). Calculate theaverage outcome of a model whena text token is included versus notincluded with Partial Dependence
for Text Tokens. Select between TFIDF or the newly added Vectorizer +Linear Model (VLM) when it comesto generating tokens for Surrogate
models.

Third Party Models•

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Explanations for time seriesmodels Time series models workwith various MLI explainers,
including Sensitivity Analysis,
Disparate Impact Analysis, Partial
Dependence/Individual Conditional
Expectation, Naïve and Kernel
Shapley, Surrogate Models and allfeature importance techniques.

•

Explanations for computer visionmodels Understand what part ofan image or video led to a specificprediction using techniques such as
Grad-CAM.

•

External model interpretability
Gain insight into models thatwere built outside of the H2O AI
Hybrid Cloud with model agnostic
explainers and feature importancecalculations.

•

Bring your own recipes
Seamless control over the entire
explainability pipeline is enabledwith API access to customize all
explainability components withPython.

DATA SHEET 2021 - H2O.ai

Low Code Application
Development Framework
Rapidly build AI prototypes and applications
with a low code framework (Python/R) thatmakes it easy to deliver innovative solutions
by seamlessly integrating backend machinelearning capabilities and front end user
experiences.

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Application Development
Reduce the time it takes to build
applications with access to bestpractices and a library of prototypes
spanning multiple industries, andsimplify management of solutions
with the ability to to see changes tothe UI in real time as you develop.

•

Low Code User Interface (UI)
Creation Easily develop consumer
facing, interactive applications
with a Python/R framework that
supports integrated UI and AI
development.

•

Machine learning integration
API access to best-in-class data
science capabilities makes it easy
to integrate machine learning
functionality into new or existing
solutions.

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Operate.
H2O.ai provides a comprehensive suite of capabilities
surrounding machine learning operations that support data
scientists and machine learning engineers in the deployment,
management and monitoring of their models in production.
Additionally, the H2O AI Hybrid Cloud provides an incredibly
flexible architecture with distributed processing, optimized
compute efficiency and the ability to deploy in the environmentof your choice. Customization is well supported with easy
integration of your own transformers, recipes and models.

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Machine Learning Operations
Automatically monitor models in real-timeand set custom thresholds to receive alerts
on prediction accuracy and data drift andguarantee deployed models are operating
as intended.

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Model Repository

Model Deployment

Model Monitoring•

Model management Create acentral place to host and manage
all experiments and its associatedartifacts, across the entire
organization. Register experiments
as models, including both autogenerated and custom metadata
to have a centralized view of allmodels.

•Target deployments Build
once and deploy to any scoring
environment.

••

Deployment modes Deploy models
within the production environment
in different modes, including multivariant (A/B), champion/challenger
and canary.

Data and Concept Drift Maintainmodel oversight and know if yourmodels are scoring on data they
were not meant to or trained on.

••

Model versioning Register
experiments as new model versions
and maintain a transparent view ofall deployed versions.

••

Leaderboard View and sort
experiments and models by key
evaluation metrics.

•

3rd party model support Manage
models trained on any 3rd party
framework, including scikit-learn,
PyTorch, TensorFlow, XGBoost,LightGBM and more, just like yournative Driverless AI models.

Scoring Models can be scored inreal-time (hosted RESTful endpoint),
in batch (supported source andtarget datastores), asynchronouslyor as streaming data.

Feature importance Receive local
explanations on which features arecontributing greatest/least to theprediction value, along with thescoring result.

•

Alerts Receive alerts andnotifications for all monitored
metrics with the ability to set
custom thresholds.

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Flexible Architecture
The H2O AI Hybrid Cloud is environment
agnostic so any company, regardlessof their existing infrastructure, canincorporate H2O.ai technologies into theirmachine learning pipelines.

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Extensible

Distributed

Scalable•

Platform agnostic Support for
Google Cloud Platform, Amazon
Web Services, Microsoft Azure, oron premises.

••

NVIDIA RAPIDS integration Highperformance computing delivered
through full NVIDIA RAPIDS
integration.

•

Custom recipe architecture
Benefit from the latest versions of
Python, RAPIDS/CUML, PyTorch,
TensorFlow, H2O, XGBoost,LightGBM, datatable, datatable,sklearn, pandas, and many more
packages. And gain full control
over them and any other Python
package with our built-in custom
recipe architecture.

Multi-node training Scalable,
distributed machine learning
backends can handle any data sizeby scaling out to multiple worker
nodes.

•

Multi-CPU/GPU training Trainmodels faster across multiple CPUs/
GPUs.

•

•

Kubernetes-based deployment
Simplifies infrastructure scalability
and maintenance by automating
cloud resource allocations.

Ampere-based GPUs Easily scale
workloads with support for theunprecedented compute andnetwork acceleration of Amperebased NVIDIA GPUs through the useof the latest CUDA runtime.

•

Platform performance API
Visualize and monitor system
usage with a publicly available API
that provides platform metrics
for resource monitoring andautoscaling of H2O AI Hybrid Cloud
multinode clusters.

•

Flexible model support Trainand deploy any H2O.ai or third
party model and customize it withPython.

•Multiple programming languages
Covers the majority of the data
science user base with clients for
Python, R and Java.

DATA SHEET 2021 - H2O.ai

Innovate.
Democratizing AI across entire organizations requires themerging of powerful machine learning models with intuitive
experiences that users of all technical abilities can consume.
The H2O AI Hybrid Cloud provides an AI AppStore designed
to simplify the delivery and consumption of complex
solutions, meaning more people can access and participate
in innovation efforts.

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AI AppStoreThe H2O AI Hybrid Cloud supports rapid
prototyping and solution development while also
fostering collaboration between technical teams
and business users. With comprehensive machinelearning capabilities, a robust explainable AI toolkit,
a low code application development frameworkand integrated machine learning operations, theH2O AI AppStore is the catalyst needed to move
you from big ideas to tangible impact.

Cross-Industry Data Science Apps•

AutoInsights Automatedinteractive, engaging, andactionable insights summarized
as easy-to-understand natural
language narratives.
•

Explore new datasets to findimportant business findings•

Find insights without writing code
or a deep analytics backgroundsuch as unusual data points, topics
in text data, and correlations
between features•

LIBOR Clause Detection
A comprehensive solution forprocessing financial contracts anddocuments using automated NLP
pipelines to identify sentencesof interest that mentions LIBOR,
insights about the context of
LIBOR sentences, and a BERT based
sentence clustering algorithm tosegment different sentences indifferent categories.

Healthcare Apps•

Covid-19 Forecast The application
uses the latest Covid-19 data toprovide geography-based forecastsof confirmed cases and deaths for
up to one month in the future.

•

Covid-19 Vaccine NLP
NLP algorithms that identify subtopics and the evolving sentiment
within opinions expressed in social
media about vaccinations.

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Covid-19 Hospitalization
Predictor Name Plan for future
hospital admissions (ED, ICU, otherinpatient) based on the latest
information Covid-19 prevalenceand vaccination rates at thehospital’s area.

Model Validation Analyze therobustness and stability of yourmachine learning pipelines to reveal
any weaknesses or vulnerabilities indata and models.
•

Understand if your data is changing
overtime using techniques like drift
and adversarial similarity•

Use backtesting to understandhow a Machine Learning model
would have performed historicallyat different points in time•

Get interactive visualizations andnarratives for efficient storytelling

Financial Service Apps•••

Visualize data and model validation
techniques to better understand
if a model can be trusted to makebusiness critical decisions

Model Monitoring Monitor yourproduction models for feature drift
to understand if they can still besafely used in production.
•

Integration with H2O MLOps to getdata science insights on deployedmodels•Understand drift for external
models, such as those deployed in
SQL database•

Use custom thresholds to integrate
your business knowledge of whendrift is a concern or not•

Explainable Anomaly Detection
Identify which data points arehighly unusual and what about
them is different from the restof the data; this can be helpful
for creating new rules to findfraudulent activities.

•

Credit Card Risk A prototype of
how to build your own machinelearning model and app front end
for predicting whether or not acustomer will pay back a credit cardon time.

•

Medical Appointments No-Show
Improve patient care and patientengagement using AI to obtain thelikelihood of appointment no-show
and gain insights on factors thatplay an important role when apatient does not honor a scheduled
appointment.

•

Medicine Reviews AI- and NLPdriven assessment of the perceived
effectiveness of medicines based
on uploaded or individually enteredpatient reviews.

•

Chest Abnormality Detection
Computer vision prototype forthe detection and identification ofdifferent types of anomalies from
X-ray images, aimed at both data
scientists and clinicians.

•

Provider Claims Fraud A solution
for payers powered by AI that
evaluates the trustworthiness ofproviders, giving explanations
on the reasons that drive theevaluation.

•

Employee Churn Prediction AI
playing a central role as part of an
overall plan to minimize employee
attrition in a prototype solutionthat visualizes predictions of
employee departure, forecast churnrates, and identify relevant factors
contained in employee data.

DATA SHEET 2021 - H2O.ai

Customer Service Apps•

Customer Churn Risk An exampleof how to build your own machinelearning model and app front end
for real-time predictive analytics on
identifying if a customer is likely toleave for a competitor. Customer
Churn is repeatedly marked as a top
use case for almost all industries.
Knowing which customers aremost likely to leave us, and more
importantly, why, can give our
support staff the right tools to help
reduce customer loss.
•

Explore Customer Data•

Allows the end-user to look at a
few rows of training data•

Shows historic trends in the datausing a third-party plotting library•View Callers•

Sends simulated new data to amodel REST endpoint every 3
seconds•

Shows the end-user the latest
predictions•

Shows distribution of predictions
by state and overall•

View the App Code•Explore the Python code used tomake this app

Business Operations Apps•

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Predictive Maintenance Get
real-time information from sensor
data to understand which parts
are likely to fail, when, and why.
The Fourth Industrial Revolution is
the next iteration of automationof traditional manufacturing andindustrial practices using modern
smart technology. This includes
the concepts of interconnectivity,
digitisation, and automation.
In this context, prognostics
and health management in the
manufacturing industry, also known
as Predictive Maintenance, aim
at minimizing maintenance costsby the assessment, prognosis,
diagnosis, and health managementof industrial equipment using
intelligent and automatedprocesses. This application provides
a prognosis and diagnosis of
manufacturing equipment using
real-time sensor data.

•

Market Basket Analysis Build andexplore market basket algorithms tocreate product recommendations
for your customers. Market Basket
Analysis helps in understanding
the relationship between various
products in a catalog andgenerating recommendations using
historic transactional data. It ispopularly used for scenarios like:•

Recommending similar products
to a particular product for better
engagement•

Recommending products relevant
to an existing cart to add during
checkout•

Recommending combination of
products that can be sold together
as an upselling offer•

Recommending substituteproducts of a particular product tomanage inventory•

Explainable Hotel Ratings•

Demand Sensing Combine
traditional demand forecasting withexternal factors such as weather
or consumer buying behavior to
improve the supply chain.
••

Demand Sensing is a methodology
and technology that leverages
new mathematical techniquesand current real-time information
to create an accurate near-futureforecast (whatever may be ofhours or days depending on how
the dynamic supply chain is) of
demand, based on current realitiesof the supply chain
Demand Sensing is fundamentallydifferent in forecasting as it uses
a much broader range of demand
signals including current datafrom the supply chain and various
mathematical models to createa more accurate forecast that
responds to a real-world event
such as Market Shifts, Weather
Changes, Natural Disasters,
Consumer Buying behaviours etc

Use model interpretability tounderstand what about a text ina review makes it look positiveor negative in sentiment. This
Hotel Review Analysis demo
allows a business user to explore
satisfaction predictions made by amachine learning model. The user
can compare the key words inthe average prediction to various
subsets of the data to understandhow different guests enjoyeddifferent types of hotels.

••

Demand sensing uses hugeamounts of data and recognizes
patterns, so supply chains haveactionable signals and can makeaccurate decisions. It helps
companies understand consumer
behaviour and variables bysynthesizing big data in real-time

Employee Churn Predict therisk of employee departure andidentify relevant factors. This app
demonstrates the use of machinelearning as part of an overall plan tominimize employee attrition. Users
can view predictions of employee
departure, forecast churn rates, andidentify relevant factors containedin employee data.

DATA SHEET 2021 - H2O.ai

Try the H2O
AI Hybrid
Cloud Today.
Visit www.h2o.ai/free18

DATA SHEET 2021 - H2O.ai