Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

Length: 276 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-11-05
ISBN-10: 1801079250
ISBN-13: 9781801079259
Sales Rank: #0 (See Top 100 Books)

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments

Key Features

Explore hyperparameter optimization and model management tools
Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases

Book Description

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.

Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You’ll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you’ll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you’ll work through examples to help you solve typical business problems.

By the end of this book, you’ll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

What you will learn

Find out what an effective ML engineering process looks like
Uncover options for automating training and deployment and learn how to use them
Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
Understand what aspects of software engineering you can bring to machine learning
Gain insights into adapting software engineering for machine learning using appropriate cloud technologies
Perform hyperparameter tuning in a relatively automated way

Who this book is for

This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you’re someone who manages or wants to understand the production life cycle of these systems, you’ll find this book useful. Intermediate-level knowledge of Python is necessary.

Introduction to ML Engineering
The Machine Learning Development Process
From Model to Model Factory
Packaging Up
Deployment Patterns and Tools
Scaling Up
Building an Example ML Microservice
Building an Extract Transform Machine Learning Use Case

Machine Learning Engineering with Python
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
Section 1: What Is ML Engineering?
Chapter 1: Introduction to ML Engineering
    Technical requirements
    Defining a taxonomy of data disciplines
        Data scientist
        ML engineer
        Data engineer
    Assembling your team
    ML engineering in the real world
    What does an ML solution look like?
        Why Python?
    High-level ML system design
        Example 1: Batch anomaly detection service
        Example 2: Forecasting API
        Example 3: Streamed classification
    Summary
Chapter 2: The Machine Learning Development Process
    Technical requirements
    Setting up our tools
        Setting up an AWS account
    Concept to solution in four steps
        Discover
        Play
        Develop
        Deploy
    Summary
Section 2: ML Development and Deployment
Chapter 3: From Model to Model Factory
    Technical requirements
    Defining the model factory
    Designing your training system
        Training system design options
        Train-run
        Train-persist
    Retraining required
        Detecting drift
        Engineering features for consumption
        Engineering categorical features
        Engineering numerical features
    Learning about learning 
        Defining the target
        Cutting your losses
        Hierarchies of automation
        Optimizing hyperparameters 
        AutoML
        Auto-sklearn
    Persisting your models
    Building the model factory with pipelines
        Scikit-learn pipelines
        Spark ML pipelines
    Summary
Chapter 4: Packaging Up
    Technical requirements
    Writing good Python
        Recapping the basics
        Tips and tricks
        Adhering to standards
        Writing good PySpark
    Choosing a style
        Object-oriented programming
        Functional programming
    Packaging your code
        Why package?
        Selecting use cases for packaging
        Designing your package
    Building your package
    Testing, logging, and error handling
        Testing
        Logging
        Error handling
    Not reinventing the wheel
    Summary
Chapter 5: Deployment Patterns and Tools
    Technical requirements
    Architecting systems
    Exploring the unreasonable effectiveness of patterns
        Swimming in data lakes
        Microservices
        Event-based designs
        Batching
    Containerizing
    Hosting your own microservice on AWS
        Pushing to ECR
        Hosting on ECS
        Creating a load balancer
    Pipelining 2.0
        Revisiting CI/CD
    Summary
Chapter 6: Scaling Up
    Technical requirements
    Scaling with Spark
        Spark tips and tricks
        Spark on the cloud
    Spinning up serverless infrastructure
    Containerizing at scale with Kubernetes
    Summary
Section 3: End-to-End Examples
Chapter 7: Building an Example ML Microservice 
    Technical requirements
    Understanding the forecasting problem
    Designing our forecasting service
    Selecting the tools
    Executing the build
        Training pipeline and forecaster
        Training and forecast handlers
    Summary
Chapter 8: Building an Extract Transform Machine Learning Use Case
    Technical requirements
    Understanding the batch processing problem
    Designing an ETML solution
    Selecting the tools
        Interfaces
        Scaling of models
        Scheduling of ETML pipelines
    Executing the build
        Not reinventing the wheel in practice
        Using the Gitflow workflow
        Injecting some engineering practices
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