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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
- By Andrew Kelleher, Adam Kelleher
- Published May 8, 2019 by Addison-Wesley Professional. Part of the Addison-Wesley Data & Analytics Series series.
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eBook (Watermarked)
- Your Price: $31.99
- List Price: $39.99
- Includes EPUB, MOBI, and PDF
- About eBook Formats
This eBook includes the following formats, accessible from your Account page after purchase:
EPUB
The open industry format known for its reflowable content and usability on supported mobile devices.
MOBI
The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.
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.
About
Features
- Practical principles and step-by-step techniques for transforming any business “ask” into actionable insight
- Includes two complete end-to-end site optimization projects, with reusable code and open source tools recommendations readers can easily adapt for their own projects
- Ideal for all junior and aspiring data scientists -- and for all software developers, engineers, and others with new responsibilities related to data science
- By Andrew and Adam Kelleher, brothers playing pivotal roles in data science and engineering at BuzzFeed
- No heavy math or statistics background required!
Description
- Copyright 2019
- Dimensions: 7" x 9-1/8"
- Pages: 288
- Edition: 1st
- Book
- ISBN-10: 0-13-411654-2
- ISBN-13: 978-0-13-411654-9
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
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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
