This page will serve as a study guide for the AWS AI Practitioner exam.
- Introduction to AI
- Introduction to Cloud Computering
- Setting up an AWS account
- AWS Console - Quick guide
- Setting up budgets
- What is GenAI?
- GenAI concepts
- What is prompt engineering?
- Prompt performance opermization
- Prompt engineering Techniques
- Overview
- AWS Bedroom overview (hands on)
- Amazon Bedrock - Foundation Models (FM)
- Amazon Bedrock - Fine tuning (FM)
- Amazon Bedrock - The different Foundation Models
- Amazon Bedrock - Retrieval-Augmented Generation (RAG)
- Amazon Bedrock - Guardrails
- Amazon Bedrock - Agents
- Amazon Bedrock - CloudWatch
- Amazon Bedrock - Pricing overview
- Amazon Bedrock - AI Stylist
- AWS SageMake - Intro
- AWS SageMake - Data Tools
- AWS SageMake - Models & Humans
- AWS SageMake - Governance
- AWS SageMake - Consoles
- What are the differences?
- Overview
- Amazon Q - Business
- Amazon Q - Apps
- Amazon Q - Developer
- Amazon Q - AWS Services
- Amazon Q - PartyRock
- Overview
- AI & ML - Common exam terms
- AI & ML - Training data
- AI & ML - Supervised learning
- AI & ML - Unsupervised learning
- AI & ML - Reinforced learning
- AI & ML - Reinforced learning with human feedback
- AI & ML - Model evaluation/Metrics
- AI & ML - Model fit, varients and bias
- AI & ML - Inferencing
- AI & ML - When not to use ML
- Hyperparatmeters
- When to use an AI Managed service 2.Amazon Comprehend 3.Amazon Translate
- Amazon Transcribe
- Amazon Polly
- Amazon Rekognition
- Amazon Forecast
- Amazon Lex
- Amazon Personalise
- Amazon Textract
- Amazon Kendra
- Amazon Mechanical Trunk
- Amazon Augemented AI
- Amazon DeepRacer
- Amazon Comprehend Medical & Transcribe
- Amazon Hardware for AI