6.2.1. Key Concepts Review: Core ML & AWS ML Landscape

First Principle: A robust understanding of core ML fundamentals and the overall AWS ML landscape is the foundation for building scalable, secure, and resilient intelligent systems.

This review consolidates core ML concepts and the AWS ML landscape.

Core ML & AWS ML Landscape Concepts:

• The ML Workflow Lifecycle: Problem definition, data (ingest, prep, EDA, feature eng), model (select, train, eval, tune), deploy, monitor, MLOps.
• Data Quality & Bias Management: Accuracy, completeness, consistency; types of bias (selection, historical, measurement); SageMaker Clarify.
• Algorithm Selection & Model Evaluation: Problem type (regression/classification/unsupervised), data characteristics; appropriate metrics (MAE/MSE/R-squared for regression, Accuracy/Precision/Recall/F1/ROC-AUC for classification, Confusion Matrix).
• Scalability & Performance for ML: Distributed training, instance types (GPU), real-time vs. batch inference, throughput, latency.
• ML Security & Governance: Data encryption (at rest/in transit, KMS), access control (IAM), network security (VPC, VPC Endpoints), auditing (CloudTrail).
• MLOps & Operational Excellence: Automation, reproducibility, monitoring, CI/CD, version control, drift detection (SageMaker Pipelines, Model Monitor).
• AWS ML Landscape:
  • Foundational Services: EC2, S3, VPC.
  • Amazon SageMaker: Studio, Notebooks, Data Wrangler, Feature Store, Processing Jobs, Training Jobs, HPO, Experiments, Model Registry, Endpoints, Model Monitor, Pipelines.
  • AI Services: Rekognition, Comprehend, Transcribe, Polly, Lex, Forecast, Personalize, Fraud Detector.
  • Other Analytics/DB Services: Kinesis, Glue, EMR, Athena, Redshift, DynamoDB.

Scenario: You need to explain the fundamental stages of an ML project and identify the relevant AWS services at each stage, from data collection to model deployment and monitoring.

Reflection Question: How does understanding core ML concepts (e.g., ML workflow lifecycle, bias management) and the comprehensive AWS ML landscape provide the foundation for building scalable, secure, and resilient intelligent systems in the cloud?