Study the official MLA-C01 domains. Each lesson teaches the concept from the AWS exam guide and first-party docs, with figures for the service choices.
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Course Content
Module 1: Domain 1. Data Preparation for Machine Learning
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Domain 1. Data Preparation for Machine Learning
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1.1 Ingest and Store Data
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1.1.1 Data Formats and Access Patterns
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1.1.2 Storage Options for ML Data
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1.1.3 Streaming Ingest
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1.2 Transform Data and Feature Engineering
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1.2.1 Data Wrangler, Glue, and DataBrew
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1.2.2 Feature Store
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1.2.3 Labeling and Annotation
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1.3 Data Integrity and Modeling Prep
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1.3.1 Bias, Quality, and Clarify
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1.3.2 Encryption, Masking, and Training Attach
Module 2: Domain 2. ML Model Development
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Domain 2. ML Model Development
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2.1 Choose a Modeling Approach
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2.1.1 Built-in Algorithms and Interpretability
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2.1.2 AI Services, JumpStart, and Bedrock
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2.2 Train and Refine Models
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2.2.1 Script Mode and Distributed Training
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2.2.2 Automatic Model Tuning and Regularization
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2.2.3 Model Registry and Versioning
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2.3 Analyze Model Performance
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2.3.1 Evaluation Metrics and Baselines
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2.3.2 Clarify, Debugger, and Shadow Variants
Module 3: Domain 3. Deployment and Orchestration of ML Workflows
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Domain 3. Deployment and Orchestration of ML Workflows
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3.1 Select Deployment Infrastructure
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3.1.1 Endpoint Types and Inference Modes
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3.1.2 Multi-Model, Multi-Container, and Neo
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3.1.3 Deployment Targets
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3.2 Script Infrastructure and Scaling
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3.2.1 IaC, Containers, and VPC
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3.3 Set Up CI/CD Orchestration
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3.3.1 Pipelines, EventBridge, and Retrain
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3.3.2 Deployment Strategies and Rollback
Module 4: Domain 4. ML Solution Monitoring, Maintenance, and Security
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Domain 4. ML Solution Monitoring, Maintenance, and Security
