AWS Certified AI Practitioner

This module covers the foundational concepts of Artificial Intelligence and Machine Learning required for the AWS Certified AI Practitioner exam. Topics include types of AI, core ML paradigms (supervised, unsupervised, reinforcement learning), deep learning and neural networks, NLP, computer vision, data splitting strategies, model performance issues such as overfitting and underfitting, the bias-variance tradeoff, AWS AI/ML service categories, and key ML terminology. Mastery of these fundamentals is essential for understanding how AWS AI services work and for selecting the right service or approach for a given business problem.

This module covers AWS AI/ML services and their real-world applications across industries. It focuses on understanding which AWS service to select for specific use cases, including computer vision, natural language processing, speech processing, document analysis, intelligent search, personalization, forecasting, fraud detection, and AI-powered developer tools. Mastery of this module requires the ability to match business scenarios to the correct AWS AI service and understand how these services integrate into industry-specific solutions for healthcare, finance, retail, and manufacturing.

This module covers responsible AI principles, bias detection and mitigation, fairness, explainability, transparency, governance, and AWS-specific tools and services for building ethical and trustworthy AI/ML systems. It prepares candidates for the AWS AI Practitioner certification by addressing regulatory considerations, human-in-the-loop approaches, model monitoring, and the practical application of responsible AI frameworks within the AWS ecosystem.

This module covers the end-to-end process of developing machine learning solutions on AWS, focusing on Amazon SageMaker and its ecosystem of tools. Topics include ML pipeline stages, SageMaker components such as Studio, built-in algorithms, training jobs, inference endpoints, model evaluation metrics, MLOps practices with Model Registry and Pipelines, data preparation with Data Wrangler and Feature Store, data labeling with Ground Truth, and the AWS infrastructure choices that underpin ML workloads.

This module covers the core concepts, services, and best practices for building generative AI solutions on AWS. It includes foundation models, Amazon Bedrock and its features (Knowledge Bases, Agents, Guardrails), Amazon Q products, LLM architecture fundamentals, generative AI application patterns, embeddings, vector databases, and no-code tools like PartyRock. Mastery of these topics is essential for the AWS AI Practitioner certification.

This module covers techniques and strategies for optimizing foundation models in AWS environments, including fine-tuning approaches (full and parameter-efficient), transfer learning, domain adaptation, Retrieval Augmented Generation (RAG), prompt engineering, Amazon Bedrock fine-tuning and continued pre-training capabilities, model evaluation and benchmarking metrics, inference optimization, inference parameter tuning, cost optimization strategies, and decision frameworks for choosing between RAG, fine-tuning, and prompt engineering.

This module covers the security, compliance, and governance aspects of AI/ML workloads on AWS. Topics include the shared responsibility model as it applies to AI services, data privacy and encryption strategies, IAM policies for SageMaker and Bedrock, network isolation with VPCs and PrivateLink, monitoring and auditing with CloudTrail, sensitive data detection with Macie, compliance frameworks relevant to AI/ML, model governance and lineage tracking, AWS Config rules, data governance, SageMaker Role Manager, KMS encryption for ML artifacts, and audit and compliance reporting. Mastering these concepts is essential for building secure, compliant, and well-governed AI solutions on AWS.

Comprehensive coverage of prompt engineering techniques, strategies, and best practices for the AWS AI Practitioner (AIF-C01) exam. This module covers fundamental prompting methods (zero-shot, few-shot, chain-of-thought), prompt structure and components, advanced techniques (role prompting, structured output, prompt chaining), inference parameters (temperature, top-p, top-k), hallucination mitigation, Amazon Bedrock prompt management, prompt security, and context window optimization.