MLOps Engineering on AWS

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