AWS Associate Machine Learning Engineer Training Course - United Kingdom 

Domain 1: Data Preparation for Machine Learning

• Ingest and Store Data

• Data Formats and Ingestion Mechanisms
• How to Use the Core AWS Data Sources
• How to Use AWS Streaming Data Sources to Ingest Data
• AWS Storage Options, Including Use Cases and Tradeoffs

• Transform Data and Perform Feature Engineering

• Data Cleaning and Transformation Techniques
• Feature Engineering Techniques
• Encoding Techniques
• Tools to Explore, Visualise, or Transform Data and Features
• Services That Transform Streaming Data
• Data Annotation and Labelling Services

• Ensure Data Integrity and Prepare Data for Modelling

• Pre-Training Bias Metrics for Numeric, Text, and Image Data
• Strategies to Address CI in Numeric, Text, and Image Datasets
• Techniques to Encrypt Data
• Data Classification, Anonymisation, and Masking
• Implications of Compliance Requirements

Domain 2: ML Model Development

• Choose a Modelling Approach

• Capabilities and Appropriate Uses of ML Algorithms to Solve Business Problems
• How to Use AWS AI Services to Solve Specific Business Problems
• How to Consider Interpretability During Model Selection or Algorithm Selection
• SageMaker Built-In Algorithms and When to Apply Them

• Train and Refine Models

• Elements in the Training Process
• Methods to Reduce Model Training Time
• Factors That Influence Model Size
• Methods to Improve Model Performance
• Benefits of Regularisation Techniques
• Hyperparameter Tuning Techniques
• Model Hyperparameters and Their Effects on Model Performance
• Methods to Integrate Models Built Outside SageMaker into SageMaker

• Analyse Model Performance

• Model Evaluation Techniques and Metrics
• Methods to Create Performance Baselines
• Methods to Identify Model Overfitting and Underfitting
• Metrics Available in SageMaker Clarify to Gain Insights into ML Training Data and Models
• Convergence Issues

Domain 3: Deployment and Orchestration of ML Workflows

• Select Deployment Infrastructure Based on Existing Architecture and Requirements

• Deployment Best Practices
• AWS Deployment Services
• Methods to Serve ML Models in Real Time and in Batches
• How to Provision Compute Resources in Production and Test Environments
• Model and Endpoint Requirements for Deployment Endpoints
• How to Choose Appropriate Containers
• Methods to Optimise Models on Edge Devices

• Create and Script Infrastructure Based on Existing Architecture and Requirements

• Difference Between On-Demand and Provisioned Resources
• How to Compare Scaling Policies
• Tradeoffs and Use Cases of IaC Options
• Containerisation Concepts and AWS Container Services
• How to Use SageMaker Endpoint Auto Scaling Policies to Meet Scalability Requirements

• Use Automated Orchestration Tools to Set Up CI/CD Pipelines

• Capabilities and Quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy
• Automation and Integration of Data Ingestion with Orchestration Services
• Version Control Systems and Basic Usage
• CI/CD Principles and How They Fit into ML Workflows
• Deployment Strategies and Rollback Actions
• How Code Repositories and Pipelines Work Together

Domain 4: ML Solution Monitoring, Maintenance, and Security

• Monitor Model Inference

• Drift in ML Models
• Techniques to Monitor Data Quality and Model Performance
• Design Principles for ML Lenses Relevant to Monitoring

• Monitor and Optimise Infrastructure and Costs

• Key Performance Metrics for ML Infrastructure
• Monitoring and Observability Tools to Troubleshoot Latency and Performance Issues
• How to Use AWS CloudTrail to Log, Monitor, and Invoke Re-Training Activities
• Differences Between Instance Types and How They Affect Performance
• Capabilities of Cost Analysis Tools
• Cost Tracking and Allocation Techniques

• Secure AWS Resources

• IAM Roles, Policies, and Groups That Control Access to AWS Services
• SageMaker Security and Compliance Features
• Controls for Network Access to ML Resources
• Security Best Practices for CI/CD Pipelines