Machine Learning in Production

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