Machine Learning at Scale with H2O: A practical guide to building and deploying machine learning models on enterprise systems

396 Pages·2022·14.352 MB·English

by  Gregory Keys, David Whiting

Machine Learning
at Scale with H2O
A practical guide to building and deploying machine
learning models on enterprise systems
Gregory Keys
David Whiting
BIRMINGHAM—MUMBAI
Machine Learning at Scale with H2O
Copyright © 2022 Packt Publishing
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My deepest love and warmth to Mary, Julia and Alexa for their support and
understanding while husband and dad disappeared to the basement for
significant chunks of nights and weekends as the seasons progressed.
- Gregory
To my wife Kathy, and son Ben, who endured too many late nights and
weekends of dad locked away in his study working; the book has been a
family effort and its culmination a family success.
- David
Acknowledgments
This book would not have been possible without the approval and support of our
respective leaders at H2O.ai at the time of its writing, Dmitry Baev and Eyal Kaldes. In
addition, we pay our great appreciation to the deep expertise of the many Makers at H2O.
ai. Their day-to-day collaboration, education, and machine learning expertise are diffused
throughout the pages of this book.
One name needs to be called out in particular: massive thanks to Eric Gudgeon for his
infinite and unrelenting technical teachings, and for defining and developing a vast
landscape of H2O model deployment implementations.
This book took longer to pull together than either of us expected. Working at a hyper-
focused and highly energized company certainly was a contributing factor. Against
this backdrop, we appreciate the world-class patience, encouragement, guidance, and
professionalism of the Packt team in collaborating on this book from start to finish.
And most importantly there is family, who unfairly signed up for book writing without
fully knowing it.
Contributors
About the authors
Gregory Keys is a master principal cloud architect for Data and AI at Oracle. Formerly
a senior solutions architect at H2O.ai, he has over 20 years of experience designing and
implementing software and data systems. He specializes in AI/ML solutions and has
multiple software patents. Gregory has a PhD in evolutionary biology, which has greatly
influenced him as a systems thinker.
David Whiting is a data science director and head of training at H2O.ai. He has a PhD
in statistics from Texas A&M University and over 25 years of professional experience in
academia, consulting, and industry. He has built and led data science teams in financial
services and other regulated enterprises.
About the reviewers
Jan Gamec is a lead software engineer at H2O.ai and one of the top contributors to a state-
of-the-art AutoML platform called Driverless AI. In the past decade, he has contributed
to various projects, focusing on machine learning, cryptography, and web technologies,
either in the public or academic sector. Jan holds a master's degree in machine learning
and computer science from CTU, Czech Republic, with the main focus of interest being
genetic programming, neural networks, and reinforcement learning.
Jagadeesh Rajarajan has over 10 years of experience in building scalable data science
systems. He has rich domain knowledge in the following areas: search relevance
(information retrieval), recommender systems, AI for customer engagement (acquisition,
activation, and retention), MLOps, and interpretable machine learning systems.
Eric Gudgeon has worked on many large complex systems, built nationwide networks,
and helped customers deploy highly scalable low-latency solutions. He has a passion for
technology and finding creative solutions to problems.
Ondrej Bilek is a lead software engineer at H2O.ai and has rich experience designing
and implementing machine learning platforms for Hadoop and Kubernetes. He led the
development of Enterprise Steam and is currently working on the H2O AI Cloud.
Table of Contents
Preface
Section 1 – Introduction to the H2O Machine
Learning Platform for Data at Scale
1
Opportunities and Challenges
ML at scale 4 The business challenge – getting
your models into enterprise
The ML life cycle and three
production systems 10
challenge areas for ML at scale 5
The navigation challenge – navigating
A simplified ML life cycle 5
the enterprise stakeholder landscape 11
The model building challenge –
state-of-the-art models at scale 6 H2O.ai's answer to
these challenges 12
Summary 14
2
Platform Components and Key Concepts
Technical requirements 16 H2O Core – in-memory distributed
model building 21
Hello World – the H2O machine
learning code 16 H2O Enterprise Steam – a managed,
self-provisioning portal 23
Code example 16
The H2O MOJO – a flexible, low-latency
Some issues of scale 19
scoring artifact 24
The components of H2O
The workflow using H2O
machine learning at scale 21
components 25
H2O key concepts 26
viii Table of Contents
The data scientist's experience 26 Sparkling Water allows users to code
The H2O cluster 28 in H2O and Spark seamlessly 33
Enterprise Steam as an H2O gateway 30 MOJOs export as
Enterprise Steam and the H2O Core DevOps-friendly artifacts 34
high-level architecture 32
Summary 35
3
Fundamental Workflow – Data to Deployable Model
Technical requirements 38 Launching an H2O cluster using
the Enterprise Steam API versus the
Use case and data overview 39
UI (step 1) 47
The fundamental workflow 39
Launching an H2O-3 versus Sparkling
Step 1 – launching the H2O cluster 39 Water cluster (step 1) 47
Step 2 – connecting to the H2O cluster 42 Implementing Enterprise Steam or
Step 3 – building the model 44 not (steps 1–2) 48
Step 4 – evaluating and explaining Using a personal access token to log in
the model 46 to Enterprise Steam (step 2) 48
Step 5 – exporting the model's Building the model (step 3) 48
scoring artifact 46 Evaluating and explaining the
Step 6 – shutting down the cluster 46 model (step 4) 49
Exporting the model's scoring
Variation points – alternatives
artifact (step 5) 50
and extensions to the
Shutting down the cluster (step 6) 50
fundamental workflow 47
Summary 50
Section 2 – Building State-of-the-Art Models
on Large Data Volumes Using H2O
4
H2O Model Building at Scale – Capability Articulation
H2O data capabilities during Exporting data out of the H2O cluster 57
model building 54 Additional data capabilities provided
by Sparkling Water 58
Ingesting data from the source to the
H2O cluster 54
H2O machine learning
Manipulating data in the H2O cluster 56
algorithms 58
Table of Contents ix
H2O unsupervised learning algorithms 59 H2O model training capabilities 62
H2O supervised learning algorithms 60 H2O model evaluation capabilities 62
Parameters and hyperparameters 60 H2O model explainability capabilities 63
H2O extensions of supervised learning 61 H2O trained model artifacts 64
Miscellaneous 61
Summary 64
H2O modeling capabilities 61
5
Advanced Model Building – Part I
Technical requirements 66 H2O AutoML 91
Splitting data for validation or The AutoML leaderboard 93
cross-validation and testing 66
Feature engineering options 95
Train, validate, and test set splits 68
Target encoding 97
Train and test splits for k-fold
Other feature engineering options 107
cross-validation 69
Leveraging H2O Flow to
Algorithm considerations 69
enhance your IDE workflow 108
An introduction to decision trees 71
Monitoring with Flow 109
Random forests 73
Interactive investigations with Flow 115
Gradient boosting 74
Baseline model training 76 Putting it all together
– algorithms, feature
Model optimization with
engineering, grid search,
grid search 84
and AutoML 121
Step 1 – a Cartesian grid search to
An enhanced AutoML procedure 121
focus on the best tree depth 86
Step 2 – a random grid search to tune Summary 122
other parameters 88
6
Advanced Model Building – Part II
Technical requirements 124 Defining Spark pipeline stages 130
Modeling in Sparkling Water 124 Creating a Sparkling Water pipeline 140
Looking ahead – a production preview 141
Introducing Sparkling Water pipelines 125
Implementing a sentiment UL methods in H2O 141
analysis pipeline 126
What is anomaly detection? 141
Importing the raw Amazon data 128