Machine Learning in Production

Length: 288 pages
Edition: 1
Language: English
Publisher: Addison-Wesley Professional
Publication Date: 2019-03-07
ISBN-10: 0134116542
ISBN-13: 9780134116549
Sales Rank: #136866 (See Top 100 Books)

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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Cover
About This E-Book
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
    Who This Book Is For
    What This Book Covers
    Going Forward
About the Authors
I: Principles of Framing
    1. The Role of the Data Scientist
        1.1 Introduction
        1.2 The Role of the Data Scientist
        1.3 Conclusion
    2. Project Workflow
        2.1 Introduction
        2.2 The Data Team Context
        2.3 Agile Development and the Product Focus
        2.4 Conclusion
    3. Quantifying Error
        3.1 Introduction
        3.2 Quantifying Error in Measured Values
        3.3 Sampling Error
        3.4 Error Propagation
        3.5 Conclusion
    4. Data Encoding and Preprocessing
        4.1 Introduction
        4.2 Simple Text Preprocessing
        4.3 Information Loss
        4.4 Conclusion
    5. Hypothesis Testing
        5.1 Introduction
        5.2 What Is a Hypothesis?
        5.3 Types of Errors
        5.4 P-values and Confidence Intervals
        5.5 Multiple Testing and “P-hacking”
        5.6 An Example
        5.7 Planning and Context
        5.8 Conclusion
    6. Data Visualization
        6.1 Introduction
        6.2 Distributions and Summary Statistics
        6.3 Time-Series Plots
        6.4 Graph Visualization
        6.5 Conclusion
II: Algorithms and Architectures
    7. Introduction to Algorithms and Architectures
        7.1 Introduction
        7.2 Architectures
        7.3 Models
        7.4 Conclusion
    8. Comparison
        8.1 Introduction
        8.2 Jaccard Distance
        8.3 MinHash
        8.4 Cosine Similarity
        8.5 Mahalanobis Distance
        8.6 Conclusion
    9. Regression
        9.1 Introduction
        9.2 Linear Least Squares
        9.3 Nonlinear Regression with Linear Regression
        9.4 Random Forest
        9.5 Conclusion
    10. Classification and Clustering
        10.1 Introduction
        10.2 Logistic Regression
        10.3 Bayesian Inference, Naive Bayes
        10.4 K-Means
        10.5 Leading Eigenvalue
        10.6 Greedy Louvain
        10.7 Nearest Neighbors
        10.8 Conclusion
    11. Bayesian Networks
        11.1 Introduction
        11.2 Causal Graphs, Conditional Independence, and Markovity
        11.3 D-separation and the Markov Property
        11.4 Causal Graphs as Bayesian Networks
        11.5 Fitting Models
        11.6 Conclusion
    12. Dimensional Reduction and Latent Variable Models
        12.1 Introduction
        12.2 Priors
        12.3 Factor Analysis
        12.4 Principal Components Analysis
        12.5 Independent Component Analysis
        12.6 Latent Dirichlet Allocation
        12.7 Conclusion
    13. Causal Inference
        13.1 Introduction
        13.2 Experiments
        13.3 Observation: An Example
        13.4 Controlling to Block Non-causal Paths
        13.5 Machine-Learning Estimators
        13.6 Conclusion
    14. Advanced Machine Learning
        14.1 Introduction
        14.2 Optimization
        14.3 Neural Networks
        14.4 Conclusion
III: Bottlenecks and Optimizations
    15. Hardware Fundamentals
        15.1 Introduction
        15.2 Random Access Memory
        15.3 Nonvolatile/Persistent Storage
        15.4 Throughput
        15.5 Processors
        15.6 Conclusion
    16. Software Fundamentals
        16.1 Introduction
        16.2 Paging
        16.3 Indexing
        16.4 Granularity
        16.5 Robustness
        16.6 Extract, Transfer/Transform, Load
        16.7 Conclusion
    17. Software Architecture
        17.1 Introduction
        17.2 Client-Server Architecture
        17.3 N-tier/Service-Oriented Architecture
        17.4 Microservices
        17.5 Monolith
        17.6 Practical Cases (Mix-and-Match Architectures)
        17.7 Conclusion
    18. The CAP Theorem
        18.1 Introduction
        18.2 Consistency/Concurrency
        18.3 Availability
        18.4 Partition Tolerance
        18.5 Conclusion
    19. Logical Network Topological Nodes
        19.1 Introduction
        19.2 Network Diagrams
        19.3 Load Balancing
        19.4 Caches
        19.5 Databases
        19.6 Queues
        19.7 Conclusion
Bibliography
Index
Credits
Code Snippets