title	AWS Machine Learning and AI Notes
scope	AWS Machine Learning and AI
certifications	MLA-C01
status	reviewed
domain	all
service	AWS AI and ML
tags	aws machine-learning artificial-intelligence bedrock sagemaker readme
aliases	AWS ML and AI Notes AWS AI Notes AWS Machine Learning Notes
last_verified	2026-05-16
source_type	aws-official

AWS Machine Learning and AI Notes

This vault is an AWS Machine Learning and AI knowledge base. It covers AWS AI services, data foundations, classic ML, SageMaker AI, Amazon Bedrock, generative AI, agentic AI, MLOps, security, governance, and certification review.

MLA-C01 is preserved as a certification track, not the only purpose of the repo. Notes that still map to the AWS Certified Machine Learning Engineer - Associate exam keep the MLA-C01 certification metadata and mla-c01 tag so they remain useful for exam review.

Primary Knowledge Paths

Path	Start Here	Purpose
AWS AI services	01-ai-services/	Applied AI services for language, search, personalization, moderation, forecasting, contact center, and domain-specific workflows
Data foundation for AI	02-data-ingestion-and-storage/, 03-data-transformation-integrity-and-feature-engineering/, 12-sql/	Data ingestion, storage, transformation, quality, feature engineering, analytics, and retrieval foundations
ML model development	04-model-training-tuning-and-evaluation/, common/	Core ML concepts, training, tuning, metrics, model selection, and evaluation
SageMaker AI	05-sagemaker-ai/, 06-sagemaker-built-in-algorithms/	Build, train, customize, deploy, govern, and operate ML models on Amazon SageMaker AI
Generative AI and Bedrock	07-generative-ai-model-fundamentals/, 08-building-gen-ai-apps-with-bedrock/, 13-bedrock/	Foundation models, Bedrock APIs, model access, customization, guardrails, knowledge bases, agents, and production patterns
Agentic AI	14-agentic-ai/, 13-bedrock/bedrock-agentcore.md	Agent design, MCP, AgentCore, multi-agent systems, Claude on AWS, agentic RAG, memory, tools, and observability
MLOps, security, and governance	09-machine-learning-operations/, 10-security-identity-and-compliance/, 11-machine-learning-best-practices/	Production operations, monitoring, deployment, governance, responsible AI, compliance, and human review
MLA-C01 certification review	00-exam-guide/	Official exam overview, domain maps, scope checklists, and study roadmap for AWS Certified Machine Learning Engineer - Associate

flowchart LR
    Data["Data foundation"] --> ML["ML model development"]
    ML --> SageMaker["SageMaker AI"]
    Data --> Bedrock["Amazon Bedrock"]
    Bedrock --> Agents["Agentic AI"]
    SageMaker --> Ops["MLOps, security, governance"]
    Agents --> Ops
    Ops --> Cert["MLA-C01 track"]

Loading

How This Vault Is Organized

Folder	Purpose
00-exam-guide/	MLA-C01 certification overview, domain maps, scope checklists, and study roadmap
01-ai-services/	AWS AI and ML application services
02-data-ingestion-and-storage/	Data sources, streaming, storage, lake, warehouse, and database services
03-data-transformation-integrity-and-feature-engineering/	Data transformation, Glue, Spark, quality, feature engineering, and integrity topics
04-model-training-tuning-and-evaluation/	Training, tuning, metrics, model selection, and evaluation concepts
05-sagemaker-ai/	Amazon SageMaker AI capabilities for build, train, tune, deploy, and govern workflows
06-sagemaker-built-in-algorithms/	SageMaker AI built-in algorithms and algorithm selection notes
07-generative-ai-model-fundamentals/	Foundation model and transformer fundamentals
08-building-gen-ai-apps-with-bedrock/	Bedrock application scenarios that link to canonical Bedrock notes
09-machine-learning-operations/	MLOps, orchestration, CI/CD, deployment, cost, and observability
10-security-identity-and-compliance/	IAM, network isolation, encryption, governance, compliance, and data protection
11-machine-learning-best-practices/	Responsible AI, Well-Architected guidance, A2I, and human review
12-sql/	SQL support material for Athena, Redshift, Glue, and analytics tasks
13-bedrock/	Canonical Amazon Bedrock and Bedrock AgentCore feature notes
14-agentic-ai/	Current agentic AI, MCP, Claude, RAG, and agent framework notes
common/	Cross-cutting ML fundamentals

Certification Track: MLA-C01

The AWS Certified Machine Learning Engineer - Associate track remains useful for structured review. AWS describes the certification as validating technical ability to implement and operationalize ML workloads in production.

Item	Value
Certification	AWS Certified Machine Learning Engineer - Associate
Exam code	MLA-C01
Duration	130 minutes
Question count	65 questions
Passing score	720
Domain 1	Data Preparation for Machine Learning, 28%
Domain 2	ML Model Development, 26%
Domain 3	Deployment and Orchestration of ML Workflows, 22%
Domain 4	ML Solution Monitoring, Maintenance, and Security, 24%

Note Status Legend

Status	Meaning
reviewed	Verified against current AWS docs and linked to applicable AWS services, concepts, or certification tracks
draft	Useful and normalized, but not yet promoted to the fully reviewed set
stale	Needs source refresh before relying on it for current implementation or certification study
legacy	AWS service or feature is no longer available to new customers, has no new releases, or is in shutdown/sunset status
supplemental	Useful background, emerging topic, or advanced material outside a specific certification's core testable scope
out-of-scope	Out of scope for a specific certification track; keep it if it is still useful for the broader AWS ML/AI knowledge base

How To Add Or Update A Note

1. Start from NOTE_TEMPLATE.md.
2. Use current AWS service names, such as Amazon SageMaker AI and Amazon Bedrock AgentCore.
3. Add frontmatter with title, scope, status, domain, service, tags, aliases, last_verified, and source_type.
4. Add certifications only when the note maps to a certification track, such as MLA-C01.
5. Include these sections: Knowledge Relevance, When To Use, Core Concepts, AWS Services And Features, Implementation Patterns, Tradeoffs And Pitfalls, Decision Triggers, Related Notes, and Sources.
6. Cite official AWS docs first. Use AWS blogs, whitepapers, release notes, or third-party sources only when AWS docs are insufficient.
7. Mark lifecycle caveats clearly. Do not delete legacy notes unless the repo owner asks for that cleanup.

Validation Checks

Run the repo validator first:

python3 scripts/validate_notes.py

Use strict section checks when intentionally migrating inherited notes to the full NOTE_TEMPLATE.md layout:

python3 scripts/validate_notes.py --strict-sections

Useful focused spot checks:

rg --files-without-match '^# ' -g '*.md'
rg --files-without-match '^(## )?(Sources|References|Additional Resources)\b' -g '*.md'
rg -n '^exam: ' -g '*.md'
rg -n 'Elastic Inference|Training Compiler|Data Pipeline|Amazon Forecast|AWS AppConfig|AWS IoT Greengrass|AWS Shield|Amazon DataZone|Kinesis Data Analytics|Studio Classic|Edge Manager|CodeWhisperer|Glue Elastic Views' -g '*.md'
rg -n 'TODO|needs-verification|source_type: "needs-verification"' -g '*.md' --glob '!README.md' --glob '!PLAN_NOTES_IMPROVEMENT.md'

Sources

• https://aws.amazon.com/ai/
• https://aws.amazon.com/ai/machine-learning/
• https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html
• https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html
• https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
• https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/
• https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html
• https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/mla-01-in-scope-services.html
• https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/mla-01-out-of-scope-services.html