Wilson B. Machine Learning Engineering in Action 2022 torrent

Textbook in PDF format

Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.
Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You’ll adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!
You’ll learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code’s architecture for improved resilience. You’ll even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective. When you’re done working through this toolbox guide, you’ll be able to reliably deliver cost-effective solutions for organizations big and small alike.
AN INTRODUCTION TO MACHINE LEARNING ENGINEERING
What is a machine learning engineer?
Your data science could use some engineering
Before you model: Planning and scoping a project
Before you model: Communication and logistics of projects
Experimentation in action: Planning and researching an ML project
Experimentation in action: Testing and evaluating a project
Experimentation in action: Moving from prototype to MVP
Experimentation in action: Finalizing an MVP with MLflow and runtime optimization
PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML
Modularity for ML: Writing testable and legible code
Standards of coding and creating maintainable ML code
Model measurement and why it’s so important
Holding on to your gains by watching for drift
ML development hubris
DEVELOPING PRODUCTION MACHINE LEARNING CODE
Writing production code
Quality and acceptance testing
Production infrastructure