What You Will Learn?
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