AWS Certified Machine Learning Study Guide - Shreyas Subramanian

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(AUC-ROC, accuracy, precision, recall, RMSE, F1 score) 9 3.5-3. Confusion matrix 9 3.5-4. Offline and online model evaluation, A/B testing 9 3.5-5. Compare models using metrics (time to train a model, quality of model, engineering costs) 9 3.5-6. Cross validation 9

      Domain 4: Machine Learning Implementation and Operations

      Subdomain 4.1: Frame Build Machine Learning Solutions for Performance, Availability, Scalability, Resiliency, and Fault Tolerance

Exam Objective Chapter
4.1-1. AWS environment logging and monitoring 8
CloudTrail and CloudWatch 8
Build Error Monitoring 8
4.1-2. Multiple regions, Multiple AZs 14
4.1-3. Docker containers 8
4.1-4. Auto Scaling groups 10
4.1-5. Rightsizing 8, 10, 12, 15
4.1-6. Load balancing 10, 15
4.1-7. AWS best practices 12, 13, 14, 15, 16

      Subdomain 4.2: Recommend and Implement the Appropriate Machine Learning Services and Features for a Given Problem

Exam Objective Chapter
4.2-1. ML on AWS (application services) 1
4.2-2. AWS service limits 1, 2
4.2-3. Build your own model vs. SageMaker built-in algorithms 8
4.2-4. Infrastructure: Instances types for ML and cost considerations 16

      Subdomain 4.3: Apply Basic AWS Security Practices to Machine Learning Solutions

Exam Objective Chapter
4.3-1. IAM 2, 13
4.3-2. S3 Bucket Policies 2, 13
4.3-3. Security groups 2, 13
4.3-4. VPC 2, 13
4.3-5. Encryption/anonymization 13

      Subdomain 4.4: Deploy and Operationalize Machine Learning Solutions

Exam Objective Chapter
4.4-1. Exposing endpoints and interacting with them 10, 11
4.4-2. ML model versioning 8, 12
4.4-3. A/B testing 10
4.4-4. Retrain pipelines 15
4.4-5. ML debugging/troubleshooting 12

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