Book Image

Machine Learning Engineering on AWS

By : Joshua Arvin Lat
Book Image

Machine Learning Engineering on AWS

By: Joshua Arvin Lat

Overview of this book

There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you’ll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You’ll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.
Table of Contents (19 chapters)
1
Part 1: Getting Started with Machine Learning Engineering on AWS
5
Part 2:Solving Data Engineering and Analysis Requirements
8
Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions
11
Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
14
Part 5:Designing and Building End-to-end MLOps Pipelines

Deleting an endpoint with a monitoring schedule

Now that we are done using our ML inference endpoint, let’s delete it, along with the attached monitors and monitoring schedules.

Follow these steps to list all the attached monitors of our ML inference endpoint and delete any attached monitoring schedules, along with the endpoint:

  1. Create a new Notebook by clicking the File menu and choosing Notebook from the list of options under the New submenu.

Note

Note that we will be creating the new notebook inside the CH08 directory beside the other notebook files we created in the previous sections of this chapter.

  1. In the Set up notebook environment window, specify the following configuration values:
    • Image: Data Science (option found under SageMaker image)
    • Kernel: Python 3
    • Start-up script: No script

Click the Select button afterward.

  1. Right-click on the tab name of the new Notebook and select Rename Notebook… from the list of options in...