AWS Certified AI Practitioner
Learn the fundamentals of artificial intelligence, machine learning, and generative AI on AWS while preparing for the AWS Certified AI Practitioner certification.
About this course
Explore the world of artificial intelligence and build a strong foundation in AI, machine learning, and generative AI with this beginner-friendly course designed to prepare you for the AWS Certified AI Practitioner (AIF-C01) exam. Start by understanding essential AI and machine learning concepts, including supervised and unsupervised learning, neural networks, natural language processing, and real-world AI applications. Discover how generative AI works, explore large language models (LLMs) and foundation models, and learn about prompt engineering, model evaluation, and retrieval-augmented generation (RAG). You'll also explore key AWS AI services, including Amazon Bedrock and Amazon SageMaker AI, and understand how organizations use these technologies to solve business problems. Learn the principles of responsible AI, including fairness, transparency, security, privacy, compliance, and AI governance. Through short, visual lessons and interactive challenges, you'll reinforce important concepts, test your knowledge, and build confidence as you progress. Whether you're new to artificial intelligence, looking to understand how AI is transforming industries, or preparing for your first AWS AI certification, this course provides an accessible introduction to AI technologies and their practical applications in the AWS Cloud.
What you will learn
AWS Certified AI Practitioner Exam Overview
- Exam Structure, Domains, and Question FormatsFree
Fundamentals of AI and Machine Learning
- AI, Machine Learning, Deep Learning, and Generative AIFree
- Machine Learning Paradigms and Data TypesFree
- Common Machine Learning Algorithms and Model FitFree
- Neural Networks and Deep Learning ArchitecturesFree
- AI Inferencing Patterns and Practical Use CasesFree
Machine Learning Development Lifecycle and MLOps
- The Machine Learning Pipeline and Amazon SageMakerFree
- Model Performance and Business MetricsFree
- MLOps and Production Model MonitoringFree
AWS Managed AI and Business Services
- Computer Vision and Document Analysis ServicesFree
- Language, Speech, and Customer Interaction ServicesFree
- Specialized Business AI: Search, Personalization, and FraudFree
- Generative Business Assistants with Amazon QFree
Fundamentals of Generative AI and Foundation Models
- Core Concepts and Representations in Generative AIFree
- The Foundation Model LifecycleFree
- Model Selection Criteria and CapabilitiesFree
- AWS Infrastructure and Generative AI ServicesFree
- The AWS Cloud Adoption Framework for AI (AWS CAF-AI)Free
Applications and Customization of Foundation Models
- Inference Parameters and Model Control
- Retrieval-Augmented Generation (RAG) and Vector Databases
- Foundation Model Customization Tradeoffs and Pricing
- Fine-Tuning Methodologies and Data Preparation
- Prompt Engineering Constructs and Advanced Prompting
- Prompt Engineering Risks, Jailbreaking, and Defenses
- Foundation Model Evaluation and Benchmarking
Guidelines for Responsible and Explainable AI
- Principles and Dimensions of Responsible AI
- Guardrails for Amazon Bedrock and Content Moderation
- Transparent versus Opaque AI Models and Tradeoffs
- Model Explainability Tools and AWS AI Documentation
- Human-Centered Design for Explainable AI
Security, Compliance, and Governance for AI Solutions
- Identity, Access Control, and Network Isolation for AI
- Data Protection, Encryption, and Secure Engineering
- Threat Detection and Vulnerability Management in AI Systems
- Data Governance, Lineage, and Lifecycle Management
- AI Compliance Standards and Auditing Services
- Cloud Financial Management and Architecture Best Practices
- Hyperparameter Tuning Strategies and Optimization
- Exploratory Data Analysis and Feature Engineering
- Edge Delivery and Caching for AI with Amazon CloudFront
- Amazon SageMaker Model Deployment Options
- In-Memory Caching and Fast Data Stores for AI
- Big Data Preparation and Lake Governance with EMR and Lake Formation