Home > Store

Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

Register your product to gain access to bonus material or receive a coupon.

Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

eBook (Watermarked)

  • Your Price: $31.99
  • List Price: $39.99
  • Includes EPUB and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.


  • Copyright 2019
  • Dimensions: 7" x 9-1/8"
  • Pages: 288
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-411657-7
  • ISBN-13: 978-0-13-411657-0

Foundational Hands-On Skills for Succeeding with Real Data Science Projects

This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings.

–From the Foreword by Paul Dix, series editor

Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory.

Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish.

The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments.

Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work.

  • Leverage agile principles to maximize development efficiency in production projects
  • Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life
  • Start with simple heuristics and improve them as your data pipeline matures
  • Avoid bad conclusions by implementing foundational error analysis techniques
  • Communicate your results with basic data visualization techniques
  • Master basic machine learning techniques, starting with linear regression and random forests
  • Perform classification and clustering on both vector and graph data
  • Learn the basics of graphical models and Bayesian inference
  • Understand correlation and causation in machine learning models
  • Explore overfitting, model capacity, and other advanced machine learning techniques
  • Make informed architectural decisions about storage, data transfer, computation, and communication

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.


Author's Site

Please visit the author's site at adamkelleher.com/ml_book.

Sample Content

Sample Pages

Download the sample pages (includes Chapter 13)

Table of Contents

Foreword xv

Preface xvii

About the Authors xxi

Part I: Principles of Framing 1

Chapter 1: The Role of the Data Scientist 3

1.1 Introduction 3

1.2 The Role of the Data Scientist 3

1.3 Conclusion 6

Chapter 2: Project Workflow 7

2.1 Introduction 7

2.2 The Data Team Context 7

2.3 Agile Development and the Product Focus 10

2.4 Conclusion 15

Chapter 3: Quantifying Error 17

3.1 Introduction 17

3.2 Quantifying Error in Measured Values 17

3.3 Sampling Error 19

3.4 Error Propagation 21

3.5 Conclusion 23

Chapter 4: Data Encoding and Preprocessing 25

4.1 Introduction 25

4.2 Simple Text Preprocessing 26

4.3 Information Loss 33

4.4 Conclusion 34

Chapter 5: Hypothesis Testing 37

5.1 Introduction 37

5.2 What Is a Hypothesis? 37

5.3 Types of Errors 39

5.4 P-values and Confidence Intervals 40

5.5 Multiple Testing and “P-hacking” 41

5.6 An Example 42

5.7 Planning and Context 43

5.8 Conclusion 44

Chapter 6: Data Visualization 45

6.1 Introduction 45

6.2 Distributions and Summary Statistics 45

6.3 Time-Series Plots 58

6.4 Graph Visualization 61

6.5 Conclusion 64

Part II: Algorithms and Architectures 67

Chapter 7: Introduction to Algorithms and Architectures 69

7.1 Introduction 69

7.2 Architectures 70

7.3 Models 74

7.4 Conclusion 77

Chapter 8: Comparison 79

8.1 Introduction 79

8.2 Jaccard Distance 79

8.3 MinHash 82

8.4 Cosine Similarity 84

8.5 Mahalanobis Distance 86

8.6 Conclusion 88

Chapter 9: Regression 89

9.1 Introduction 89

9.2 Linear Least Squares 96

9.3 Nonlinear Regression with Linear Regression 105

9.4 Random Forest 109

9.5 Conclusion 115

Chapter 10: Classification and Clustering 117

10.1 Introduction 117

10.2 Logistic Regression 118

10.3 Bayesian Inference, Naive Bayes 122

10.4 K-Means 125

10.5 Leading Eigenvalue 128

10.6 Greedy Louvain 130

10.7 Nearest Neighbors 131

10.8 Conclusion 133

Chapter 11: Bayesian Networks 135

11.1 Introduction 135

11.2 Causal Graphs, Conditional Independence, and Markovity 136

11.3 D-separation and the Markov Property 138

11.4 Causal Graphs as Bayesian Networks 142

11.5 Fitting Models 143

11.6 Conclusion 147

Chapter 12: Dimensional Reduction and Latent Variable Models 149

12.1 Introduction 149

12.2 Priors 149

12.3 Factor Analysis 151

12.4 Principal Components Analysis 152

12.5 Independent Component Analysis 154

12.6 Latent Dirichlet Allocation 159

12.7 Conclusion 165

Chapter 13: Causal Inference 167

13.1 Introduction 167

13.2 Experiments 168

13.3 Observation: An Example 171

13.4 Controlling to Block Non-causal Paths 177

13.5 Machine-Learning Estimators 182

13.6 Conclusion 187

Chapter 14: Advanced Machine Learning 189

14.1 Introduction 189

14.2 Optimization 189

14.3 Neural Networks 191

14.4 Conclusion 201

Part III: Bottlenecks and Optimizations 203

Chapter 15: Hardware Fundamentals 205

15.1 Introduction 205

15.2 Random Access Memory 205

15.3 Nonvolatile/Persistent Storage 206

15.4 Throughput 208

15.5 Processors 209

15.6 Conclusion 212

Chapter 16: Software Fundamentals 213

16.1 Introduction 213

16.2 Paging 213

16.3 Indexing 214

16.4 Granularity 214

16.5 Robustness 216

16.6 Extract, Transfer/Transform, Load 216

16.7 Conclusion 216

Chapter 17: Software Architecture 217

17.1 Introduction 217

17.2 Client-Server Architecture 217

17.3 N-tier/Service-Oriented Architecture 218

17.4 Microservices 220

17.5 Monolith 220

17.6 Practical Cases (Mix-and-Match Architectures) 221

17.7 Conclusion 221

Chapter 18: The CAP Theorem 223

18.1 Introduction 223

18.2 Consistency/Concurrency 223

18.3 Availability 225

18.4 Partition Tolerance 231

18.5 Conclusion 232

Chapter 19: Logical Network Topological Nodes 233

19.1 Introduction 233

19.2 Network Diagrams 233

19.3 Load Balancing 234

19.4 Caches 235

19.5 Databases 238

19.6 Queues 241

19.7 Conclusion 243

Bibliography 245

Index 247


Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020