MLOps Engineering on AWS

This MLOps Engineering on AWS training builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators. MLOps Engineering on AWS Benefits In this course, you will learn how 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. Training Prerequisites Learning Tree course 1226, AWS Technical Essentials Learning Tree course 1222, DevOps Engineering on AWS, or equivalent experience  Practical Data Science with Amazon SageMaker course, or equivalent experience  MLOps Engineering on AWS Training Outline Day 1 Module 0: Welcome Course introduction Module 1: Introduction to MLOps Machine learning operations Goals of MLOps Communication From DevOps to MLOps ML workflow Scope MLOps view of ML workflow MLOps cases Module 2: MLOps Development Intro to build, train, and evaluate machine learning models MLOps security Automating Apache Airflow Kubernetes integration for MLOps Amazon SageMaker for MLOps Lab: Bring your own algorithm to an MLOps pipeline Demonstration: Amazon SageMaker Intro to build, train, and evaluate machine learning models Lab: Code and serve your ML model with AWS CodeBuild Activity: MLOps Action Plan Workbook Day 2 Module 3: MLOps Deployment  Introduction to deployment operations Model packaging Inference Lab: Deploy your model to production SageMaker production variants Deployment strategies Deploying to the edge Lab: Conduct A/B testing Activity: MLOps Action Plan Workbook  Day 3 Module 4: Model Monitoring and Operations Lab: Troubleshoot your pipeline The importance of monitoring Monitoring by design Lab: Monitor your ML model Human-in-the-loop Amazon SageMaker Model Monitor Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store Solving the Problem(s) Activity: MLOps Action Plan Workbook Module 5: Wrap-up Course review Activity: MLOps Action Plan Workbook Wrap-up