Chapter 8:
Model Deployment
Learning Objectives
By the end of this chapter, you will be able to:
- Deploy an ML model as an API using the R plumber package
- Develop serverless APIs using AWS SageMaker, AWS Lambda, and AWS API Gateway
- Create infrastructure from scratch using AWS CloudFormation
- Deploy an ML model as an API using Docker containers
In this chapter, we will learn how to host, deploy, and manage models on AWS and Docker containers.
Introduction
In the previous chapter, we studied model improvements and explored the various techniques within hyperparameter tuning to improve model performance and develop the best model for a given use case. The next step is to deploy the machine learning model into production so that it can be ...
Get Applied Supervised Learning with R now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
