MLOps Engineering on AWS Training

Duration

Prerequisites

• AWS Technical Essentials course (classroom or digital)
• DevOps Engineering on AWS course, or equivalent experience
• Practical Data Science with Amazon SageMaker course, or equivalent experience
• Recommended: The Elements of Data Science (digital course), or equivalent experience
• Recommended: Machine Learning Terminology and Process (digital course)

Target Audience

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

Skills Gained

• 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