MLOps Engineering on AWS

ACTIVITES

This course includes presentations, labs, demonstrations, workbooks, and group exercises.

COURSE OBJECTIVES

In this course, you will learn to:

• Describe machine learning operations
• Understand the key differences between DevOps and MLOps
• Describe the machine learning workflow
• Discuss the importance of communications in MLOps
• Explain end-to-end options for automation of ML workflows
• List key Amazon SageMaker features for MLOps automation
• Build an automated ML process that builds, trains, tests, and deploys models
• Build an automated ML process that retrains the model based on change(s) to the model code
• Identify elements and important steps in the deployment process
• Describe items that might be included in a model package, and their use in training or inference
• Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
• Differentiate scaling in machine learning from scaling in other applications
• Determine when to use different approaches to inference
• Discuss deployment strategies, benefits, challenges, and typical use cases
• Describe the challenges when deploying machine learning to edge devices
• Recognize important Amazon SageMaker features that are relevant to deployment and inference
• Describe why monitoring is important
• Detect data drifts in the underlying input data
• Demonstrate how to monitor ML models for bias
• Explain how to monitor model resource consumption and latency
• Discuss how to integrate human-in-the-loop reviews of model results in production

INTENDED AUDIENCE

This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud:

• DevOps engineers
• ML engineers
• Developers/operations with responsibility for operationalizing ML models

PREREQUISITES

Required

• AWS Technical Essentials course ( AWS Technical Essentials) or AWS Skill Builder )
• DevOps Engineering on AWS | Classroom Training | AWS (amazon.com) course, equivalent experience
• Practical Data Science with Amazon SageMaker | Classroom Training | AWS course, or equivalent experience

Recommended

• AWS Skill Builder (digital course), or equivalent experience
• Machine Learning Terminology and Process (digital course)