AWS Certified Machine Learning Engineer - Associate (MLA-C01): Deployment and Orchestration of ML Workflows
The third domain of the Machine Learning Engineer Associate certification exam is Deployment and Orchestration of ML Workflows. This course will teach you the concepts and skills needed to pass this domain of the exam.
What you'll learn
After developing, training, and refining a machine learning model, you’ll be ready to deploy it in a production setting. In this course, AWS Certified Machine Learning - Associate (MLA-C01): Deployment and Orchestration of ML Workflows, you’ll learn how to deploy and orchestrate machine learning models. First, you’ll explore how to select the appropriate deployment infrastructure. Next, you’ll discover how to create and script infrastructure. Finally, you’ll learn how to use automated orchestration tools to set up continuous integration and continuous delivery pipelines. When you’re finished with this course, you’ll have the skills and knowledge of machine learning on AWS needed to excel in this domain of the certification exam.
Table of contents
- Model Deployment Best Practices 2m
- Overview of Deploying Machine Learning Models Using Amazon SageMaker 2m
- Demo: Deploying a Real-time Model Endpoint Using SageMaker 10m
- When to Use Real Time Endpoints 2m
- When to Use Serverless Endpoints 2m
- Demo: Deploying a Serverless Model Endpoint Using Amazon SageMaker 7m
- When to Use Asynchronous Endpoints 2m
- When to Use Batch Inference 2m
- Demo: Using Batch Inference with SageMaker 9m
- SageMaker Built-in Container Images vs. Custom Container Images 4m
- What Is SageMaker Neo? 3m
- Demo: Deployment Orchestration with SageMaker Pipelines 11m
- Evaluating Performance Cost and Latency Tradeoffs 3m
- Selecting Appropriate Compute Resources 5m
- When to Use Apache Airflow 3m
- Selecting Multi-model or Multi-container Deployments 2m
- Containerization Concepts and AWS Container Services 3m
- Selecting the Correct Deployment Target 5m
- Select Deployment Infrastructure Based on Existing Architecture and Requirements Review Part 1 3m
- Select Deployment Infrastructure Based on Existing Architecture and Requirements Review Part 2 5m
- On Demand and Provisioned Resources 3m
- How to Compare Scaling Policies 2m
- Infrastructure as Code Using AWS CloudFormation 3m
- When to Use AWS CDK (Cloud Development Kit) 2m
- Enabling Communication Between Stacks 2m
- How to Use Sagemaker Endpoint Auto Scaling Policies 3m
- SageMaker Managed Spot Training 2m
- Building and Maintaining Containers 3m
- Demo: Building Your Own Container to Use with SageMaker 9m
- Configuring SageMaker Endpoints within the VPC Network 2m
- Understanding the SageMaker SDK 3m
- Selecting the Right Auto Scaling Metrics 3m
- Create and Script Infrastructure Based on Existing Architecture and Requirements Review Part1 4m
- Create and Script Infrastructure Based on Existing Architecture and Requirements Review Part2 6m
- Understanding CI/CD Principles 4m
- What Is AWS CodePipeline? 3m
- What Is AWS CodeBuild? 2m
- What Is AWS CodeDeploy? 2m
- Demo: Using CodePipeline 10m
- Automated Data Integration and Ingestion 4m
- Version Control Using Git 2m
- How Code Repositories and Pipelines Work Together 3m
- Invoking Pipelines 4m
- Blue Green Deployment Strategy 1m
- Canary Deployment Strategy 1m
- Linear Deployment Strategy 1m
- Automating Model Build and Deployment 1m
- Automation Using Amazon EventBridge 2m
- Using SageMaker Pipelines 3m
- Creating Automated Tests in CI/CD Pipelines 2m
- Best Practices for Retraining Models 2m
- Use Automated Orchestration Tools to Set up CI/CD Pipelines Review - Part 1 5m
- Use Automated Orchestration Tools to Set up CI/CD Pipelines Review - Part 2 4m
- Reviewing the Deployment and Orchestration of ML Workflows Domain 9m
- Deployment and Orchestration of ML Workflows Question Review: Question 1 2m
- Deployment and Orchestration of ML Workflows Question Review: Question 2 2m
- Deployment and Orchestration of ML Workflows Question Review: Question 3 2m
- Deployment and Orchestration of ML Workflows Question Review: Question 4 2m
- Deployment and Orchestration of ML Workflows Question Review: Question 5 2m
- Deployment and Orchestration of ML Workflows Question Review: Question 6 2m
About the author
Faye Ellis is Principal Training Architect, specialising in AWS, responsible for the AWSCertified Developer Associate, SysOps Administrator Associate and Security Specialty courses. Her other courses include Hands-On Troubleshooting With AWS, Hands-On Chaos Engineering, and EKS Basics. She also researches, writes and presents AWS This Week, a YouTube show designed to summarise the latest AWS updates. Faye has worked in the IT industry for around 20 years, working in SysOps, DevOps and Archit... more