Book Image

Engineering MLOps

By : Emmanuel Raj
Book Image

Engineering MLOps

By: Emmanuel Raj

Overview of this book

Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. 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.
Table of Contents (18 chapters)
1
Section 1: Framework for Building Machine Learning Models
7
Section 2: Deploying Machine Learning Models at Scale
13
Section 3: Monitoring Machine Learning Models in Production

Chapter 8: APIs and Microservice Management

In this chapter, you will learn about APIs and microservice management. So far, we have deployed ML applications that are served as APIs. Now we will look into how to develop, organize, manage, and serve APIs. You will learn the principles of API and microservice design for ML inference so that you can design your own custom ML solutions.

In this chapter, we will learn by doing as we build a microservice using FastAPI and Docker and serve it as an API. For this, we will go through the fundamentals of designing an API and microservice for an ML model trained previously (in Chapter 4, Machine Learning Pipelines). Lastly, we will reflect on some key principles, challenges, and tips to design a robust and scalable microservice and API for test and production environments. The following topics will be covered in this chapter:

  • Introduction to APIs and microservices
  • The need for microservices for ML
  • Old is gold – REST...