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Machine Learning Engineering in Action

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

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Kubernetes Native Microservices with Quarkus, and MicroProfile

Build fast, efficient Kubernetes-based Java applications using the Quarkus framework, MicroProfile, and Java standards. In Kubernetes Native Microservices with Quarkus and MicroProfile you'll learn how to: Deploy enterprise Java applications on Kubernetes Develop applications using the Quarkus runtime Compile natively using GraalVM for blazing speed Create efficient microservices applications Take advantage of MicroProfile specifications Popular Java frameworks like Spring were designed long before Kubernetes and the microservices revolution. Kubernetes Native Microservices with Quarkus and MicroProfile introduces next generation tools that have been cloud-native and Kubernetes-aware right from the beginning. Written by veteran Java developers John Clingan and Ken Finnigan, this book shares expert insight into Quarkus and MicroProfile directly from contributors at Red Hat. You'll learn how to utilize these modern tools to create efficient enterprise Java applications that are easy to deploy, maintain, and expand. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build microservices efficiently with modern Kubernetes-first tools! Quarkus works naturally with containers and Kubernetes, radically simplifying the development and deployment of microservices. This powerful framework minimizes startup time and memory use, accelerating performance and reducing hosting cost. And because it's Java from the ground up, it integrates seamlessly with your existing JVM codebase. About the book Kubernetes Native Microservices with Quarkus and MicroProfile teaches you to build microservices using containers, Kubernetes, and the Quarkus framework. You'll immediately start developing a deployable application using Quarkus and the MicroProfile APIs. Then, you'll explore the startup and runtime gains Quarkus delivers out of the box and also learn how to supercharge performance by compiling natively using GraalVM. Along the way, you'll see how to integrate a Quarkus application with Spring and pick up pro tips for monitoring and managing your microservices. What's inside Deploy enterprise Java applications on Kubernetes Develop applications using the Quarkus runtime framework Compile natively using GraalVM for blazing speed Take advantage of MicroProfile specifications About the reader For intermediate Java developers comfortable with Java EE, Jakarta EE, or Spring. Some experience with Docker and Kubernetes required. About the author John Clingan is a senior principal product manager at Red Hat, where he works on enterprise Java standards and Quarkus. Ken Finnigan is a senior principal software engineer at Workday, previously at Red Hat working on Quarkus. Table of Contents PART 1 INTRODUCTION 1 Introduction to Quarkus, MicroProfile, and Kubernetes 2 Your first Quarkus application PART 2 DEVELOPING MICROSERVICES 3 Configuring microservices 4 Database access with Panache 5 Clients for consuming other microservices 6 Application health 7 Resilience strategies 8 Reactive in an imperative world 9 Developing Spring microservices with Quarkus PART 3 OBSERVABILITY, API DEFINITION, AND SECURITY OF MICROSERVICES 10 Capturing metrics 11 Tracing microservices 12 API visualization 13 Securing a microservice

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Price: 41.99 EUR excl. shipping