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Automate Machine Learning with H2O Driverless

AI on Dell Infrastructure

Dell Validated Design for AI

July 2022

H19252

White Paper

Abstract

This

technical white paper discusses the benefits of automated machine learning

and the challenges of non

-automated model development that it overcomes. The

paper presents an

overview of the H2O Driverless AI product from H2O.ai, along

with

a solution architecture for H2O

Driverless AI built on the Dell Validated Design

for AI

. It also provides several validated use cases using the solution.

Dell Technologies Solutions

Contents

Automate Machine Learning with H2O Driverless AI on Dell Infrastructure

Dell Validated Design for AI

White Paper

Contents

Introduction ................................................................................................................................... 5

Executive summary .................................................................................................................... 5

Document purpose ..................................................................................................................... 6

Audience .................................................................................................................................... 6

The challenges of AI adoption ...................................................................................................... 6

Machine learning challenges ...................................................................................................... 6

Talent ..................................................................................................................................... 6

Time ....................................................................................................................................... 6

Trust ....................................................................................................................................... 7

Overview of AutoML and H2O Driverless AI ................................................................................ 7

AutoML workflow with H2O Driverless AI .................................................................................... 7

Key features ............................................................................................................................. 10

Solution architecture for AutoML ............................................................................................... 11

Kubernetes-based deployment using Enterprise Steam ........................................................... 11

Docker image ........................................................................................................................... 12

Security .................................................................................................................................... 12

GPU support ............................................................................................................................. 12

Storage and network configuration ........................................................................................... 13

Licensing .................................................................................................................................. 13

Invoking H2O Driverless AI from cnvrg.io MLOps Platform ..................................................... 13

AutoML on an optimized Dell infrastructure ............................................................................. 15

Sizing of AutoML infrastructure ................................................................................................. 16

Validated use cases for AutoML ................................................................................................ 17

Sentiment analysis with NLP .................................................................................................... 17

Image classification .................................................................................................................. 20

Dell Technologies services and support ................................................................................... 21

Deployment and support ........................................................................................................... 21

The Dell Technologies Customer Solutions Center .................................................................. 22

Conclusion................................................................................................................................... 22

We value your feedback ........................................................................................................... 23

References ................................................................................................................................... 24

Introduction

Automate Machine Learning with H2O Driverless AI on Dell Infrastructure

Dell Validated Design for AI

White Paper

Introduction

Artificial intelligence (AI) and machine learning have revolutionized how organizations are

using their data. Automated machine learning (AutoML) facilitates and improves the end-

to-end data science process. This process includes everything from preprocessing and

cleaning the data, selecting and engineering appropriate features, tuning and optimizing

the model, analyzing results, explaining and documenting the model, and of course,

deploying it into production.

AutoML accelerates your AI initiatives by providing methods and processes to make

machine learning accessible to both experts and nonexperts alike. Organizations looking

to apply machine learning quickly and accurately without employing large numbers of data

scientists can benefit from AutoML capabilities. For organizations that have data

scientists, AutoML equips and empowers them to create more robust models with

accuracy, speed, and transparency to deliver better performance and outcomes. In all

cases, AutoML helps organizations quickly discover business value hidden inside their

data and easily use that data to address complex problems.

H2O Driverless AI is a comprehensive automated machine learning product that uses AI

to do AI, optimizing data science workflows to increase both the quantity and quality of

data science projects delivered to business stakeholders. It empowers data scientists to

work on projects faster and more efficiently by using automation to accomplish key

machine learning tasks in minutes or hours, not months.

H2O Driverless AI provides capabilities such as:

• Exploratory data analysis (AutoViz)

• Automatic feature engineering

• Model building and validation

• Automatic model documentation (AutoDoc)

• Model selection and deployment

• Machine learning interpretability (MLI)

AutoML does not replace machine learning operations (MLOps). AutoML focuses on

automating and accelerating the model development portion of the ML pipeline, while

MLOps provides an overall life cycle management framework for data preparation, model

development, and coding. AutoML complements MLOps and can run successfully and

efficiently with various MLOps frameworks such as cnvrg.io. MLOps provides an overall

life cycle management framework for data preparation, model development, and coding.

With H2O Driverless AI bring-your-own recipes, and time series and automatic pipeline

generation for model scoring, H2O Driverless AI provides companies with an extensible

and customizable data science platform that addresses the needs of various use cases

for every enterprise in every industry.

Executive

summary

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