Download - Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale - PDF, ePUB
MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with realworld examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
You will begin by familiarizing yourself with MLOps workflow and start writing programs to train ML models. You’ll then move on to explore options for serializing and packaging ML models post-training to deploy them in production to facilitate machine learning inference. Next, you will learn about monitoring ML models and system performance using an explainable monitoring framework. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.
By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.
Who this book is for
This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.
- CC BY-NC-SA 3.0 PH
- The author's reference is not required