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

by Andrew Kelleher, Adam Kelleher

Overview

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 visualisations, 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 optimisation 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 maximise development efficiency in production projects
• Learn from practical Python code examples and visualisations 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 visualisation 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

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Product Details

ISBN-13:	9780134116563
Publisher:	Pearson Education
Publication date:	02/27/2019
Series:	Addison-Wesley Data & Analytics Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	288
File size:	19 MB
Note:	This product may take a few minutes to download.
Age Range:	18 Years

About the Author

Andrew Kelleher is a staff software engineer and distributed systems architect at Venmo. He was previously a staff software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of FastCompany's most creative people two years in a row.

 

Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches causal inference and machine learning products at Columbia. He graduated from Clemson University with a BS in physics, and has a PhD in cosmology from University of North Carolina at Chapel Hill.

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