Implementing and Operating AWS Machine Learning Solutions
Training a machine learning model is only the first step. This course will teach you how to deploy, monitor, and scale your machine learning solution in preparation for the Machine Learning Specialty exam.
What you'll learn
Machine Learning Implementation and Operations is one of the four domains covered by the AWS Machine Learning Specialty certification exam. In this course, Implementing and Operating AWS Machine Learning Solutions, you’ll learn key areas from this domain that are covered in the exam. First, you’ll explore the different AWS services that can support a machine learning solution in production. Next, you’ll discover how to deploy and scale a machine learning model with Amazon Sagemaker. Finally, you’ll learn how to implement security best practices for your machine learning solution with AWS. When you’re finished with this course, you’ll have the skills and knowledge in this domain needed to prepare for the AWS Machine Learning Specialty certification exam.
Table of contents
- Fault Tolerance and High Availability 1m
- Scaling SageMaker Endpoints 4m
- Configuring Autoscaling for Endpoints 3m
- Deployment Methodologies 2m
- Monitoring with CloudWatch and CloudTrail 6m
- Fault Tolerant SageMaker Endpoints 4m
- Loosely Coupled Architecture 6m
- Deep Learning Containers 2m
- Preparing for the Exam 1m
About the author
David is a Webby Award winning cloud development consultant that focuses on cloud native web, mobile, and IoT applications. For over fifteen years as a consultant David has led custom software development on emerging platforms for companies such as FedEx, AT&T, Sony Music, Intel, Comcast, Herman Miller, Principal Financial, and Adobe (as well as many others). David regularly writes and speaks on the digital landscape with published works for O'Reilly and Lynda.com. He has written for Mashable,... more