AWS Certified Machine Learning Engineer - Associate (MLA-C01): ML Solution Monitoring, Maintenance, and Security
This course will teach you the concepts and skills needed to pass the Machine Learning Solution Monitoring, Maintenance, and Security domain of the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.
What you'll learn
Machine learning in AWS adheres to the same guiding security principle of least privilege. Additionally, it is important to monitor ML workloads for performance and security issues. In this course, AWS Certified Machine Learning Engineer - Associate (MLA-C01): ML Solution Monitoring, Maintenance, and Security, you’ll learn to monitor, maintain, and secure machine learning workloads, and additionally, optimize these workloads for performance and cost savings. First, you’ll explore monitoring machine learning workloads. Next, you’ll discover how to balance the often opposing goals of high performance and cost in machine learning solutions. Finally, you’ll learn how to use various AWS security services to secure machine learning workloads. 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
- What Is Drift and How Do You Monitor It? 8m
- Monitor Data and Model Performance with SageMaker Model Monitor 7m
- Machine Learning Lens for Monitoring Overview 5m
- Machine Learning Anomaly Detection 6m
- Bias Detection with SageMaker Clarify 4m
- A/B Testing Production ML Models 3m
- Monitor Model Inference Exam Tips 3m
- Resilient SageMaker Models 7m
- Tools to Monitor and Troubleshoot ML Performance 4m
- Demo: CloudWatch with Sagemaker 3m
- CloudTrail Log Monitoring of ML Activites 3m
- Demo: CloudTrail with SageMaker 2m
- Selecting EC2 Instances for ML Tasks 6m
- Cost Analysis Tools for Machine Learning 2m
- Demo: Tagging and Cost Allocation 2m
- Resource Tagging for ML Environments 2m
- CloudWatch for Troubleshooting ML Workloads 3m
- Dashboards for ML Performance Monitoring 4m
- Demo: CloudWatch Anomaly Detection 2m
- Using EventBridge Events to Monitor ML Infrastructure 3m
- Demo: EventBridge for SageMaker 6m
- Monitoring and Troubleshooting ML Latency Issues 3m
- Monitor and Optimize Infrastructure and Cost Review: Part 1 6m
- Monitor and Optimize Infrastructure and Cost Review: Part 2 6m
- IAM Techniques to Control Access to ML Services 7m
- Configuring User and Service Access to ML Systems 3m
- Demo: SageMaker Execution Role 4m
- Demo: SageMaker Role Manager 7m
- Security and Compliance in SageMaker 5m
- Using VPCs for Machine Learning 4m
- Security Best Practices for CI/CD Pipelines 4m
- Ensure Least Privilege to ML Artifacts 4m
- Sagemaker Endpoint Security and Access Issues 4m
- Securing AWS Resources Exam Tips 7m
About the author
Craig has over 20 years of experience in IT in areas ranging from Systems and Network Engineering, Software Development, Technical Project Management, and Amazon Web Services. He has been creating content in Amazon Web Services for over 5 years. Areas of expertise include AWS Certified DevOps Professional, CloudFormation, and the AWS Developer Tools Suite. Craig holds 6 AWS Certifications as well as a Project Management Professional certification.