Module 1
AWS ML Services Overview
AWS ML Services Overview
8h
Leçons vidéo
Exercices
Lab pratique
Amazon SageMaker (Studio, Training, Inference)
Amazon Bedrock (GenAI/LLMs - Claude, Llama, etc.)
AWS AI Services (Rekognition, Comprehend, Textract)
Amazon Q Developer
Infrastructure AWS pour ML (EC2, S3, Lambda, ECS/EKS)
Module 2
Data Engineering for ML
Data Engineering for ML
10h
Leçons vidéo
Exercices
Lab pratique
Amazon S3 et Data Lakes
AWS Glue (ETL, Data Catalog)
Amazon Athena, Redshift
Feature Store (SageMaker)
Data Wrangler, EMR
Data versioning et lineage
Module 3
Model Development
Model Development
12h
Leçons vidéo
Exercices
Lab pratique
SageMaker Training Jobs
Built-in algorithms
Custom training (containers, scripts)
Hyperparameter tuning
SageMaker JumpStart (foundation models)
Amazon Bedrock (fine-tuning, RAG, agents)
Model Registry et versioning
Module 4
MLOps et CI/CD
MLOps et CI/CD
10h
Leçons vidéo
Exercices
Lab pratique
SageMaker Pipelines
CI/CD avec CodePipeline, CodeBuild
Model monitoring (SageMaker Model Monitor)
A/B testing et canary deployments
Infrastructure as Code (CDK, CloudFormation)
Governance (IAM, SageMaker Role Manager)
Module 5
Deployment Patterns
Deployment Patterns
8h
Leçons vidéo
Exercices
Lab pratique
Real-time inference (endpoints)
Batch transform
Serverless inference
Async inference
Multi-model endpoints
Edge deployment (SageMaker Edge Manager, IoT Greengrass)
Module 6
GenAI avec AWS
GenAI avec AWS
8h
Leçons vidéo
Exercices
Lab pratique
Amazon Bedrock foundations
Prompt engineering et RAG
Bedrock Agents et Knowledge Bases
Fine-tuning de foundation models
Guardrails et content filtering
Cost optimization pour GenAI
Module 7
Security et Governance
Security et Governance
4h
Leçons vidéo
Exercices
Lab pratique
IAM policies pour ML
VPC, encryption (KMS)
SageMaker Studio security
Compliance et audit (CloudTrail)
Data privacy