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Course Details
Course Details
What You'll Learn
This course prepares you for the AIF-C01 certification exam, covering all official exam domains and their approximate weightings:
Domain 1 Domain 1: Fundamentals of AI and ML (20%)
- Explain basic AI/ML terminology (AI, ML, deep learning, neural networks, NLP, LLM, GenAI, agentic AI) and differentiate AI, ML, GenAI, deep learning, and agentic AI
- Describe types of AI/ML learning (supervised, unsupervised, reinforcement) and types of data/inferencing (batch, real-time, labeled/unlabeled, etc.)
- Identify practical AI/ML use cases, select appropriate techniques (regression, classification, clustering), and recognize when AI/ML is NOT appropriate
- Explain capabilities of AWS managed AI/ML services (Amazon SageMaker AI, Transcribe, Translate, Comprehend, Lex, Polly)
- Describe the AI/ML development lifecycle/pipeline and MLOps fundamentals (experimentation, monitoring, retraining, production readiness)
- Describe model performance metrics (accuracy, precision, recall, F1 score) and business metrics (cost per user, ROI) to evaluate ML models
Domain 2 Domain 2: Fundamentals of GenAI (24%)
- Define foundational GenAI concepts (tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, diffusion models)
- Identify GenAI use cases (image/video/audio generation, summarization, AI assistants, code generation, customer service agents)
- Describe the foundation model (FM) lifecycle: data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback
- Understand capabilities and limitations of GenAI for business problems (hallucinations, interpretability, nondeterminism) and model selection factors
- Describe AWS infrastructure/services for building GenAI applications (Amazon Bedrock, SageMaker AI/JumpStart, Strands Agents, Bedrock AgentCore)
- Describe the token-based pricing model and cost tradeoffs of AWS GenAI services
Domain 3 Domain 3: Applications of Foundation Models (28%)
- Identify FM selection criteria (cost, modality, latency, model size, customization) and the effect of inference parameters (e.g. temperature)
- Define Retrieval Augmented Generation (RAG) and identify AWS vector database services (Amazon OpenSearch Service, Aurora, Neptune, RDS for PostgreSQL)
- Choose effective prompt engineering techniques (chain-of-thought, zero-shot, few-shot, prompt templates) and recognize risks (prompt injection, jailbreaking, hijacking)
- Describe FM training and fine-tuning methods (instruction tuning, transfer learning, continuous pre-training, RLHF) and data preparation practices
- Describe methods/metrics to evaluate FM performance (ROUGE, BLEU, BERTScore, LLM-as-a-judge, benchmark datasets, human-in-the-loop)
- Identify approaches to evaluate FM-based applications (RAG, agents, workflows) against business objective alignment metrics
Domain 4 Domain 4: Guidelines for Responsible AI (14%)
- Identify features of responsible AI: bias, fairness, inclusivity, robustness, safety, veracity
- Explain tools to identify/monitor bias, trustworthiness, and truthfulness (Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon A2I, Bedrock Guardrails)
- Identify legal and ethical risks of working with GenAI (IP infringement claims, biased model outputs, hallucinations, loss of customer trust)
- Recognize the importance of transparent and explainable models and related tools (SageMaker Model Cards, Bedrock Model Evaluations)
- Describe tradeoffs between model safety and transparency, and principles of human-centered design for explainable AI
Domain 5 Domain 5: Security, Compliance, and Governance for AI Solutions (14%)
- Identify AWS services/features to secure AI systems (IAM roles/policies, encryption, Amazon Macie, AWS PrivateLink, Bedrock Guardrails, Bedrock AgentCore Identity)
- Describe security and privacy considerations for AI systems (prompt injection, data leakage prevention, output filtering/validation, audit trail and logging)
- Describe hallucination detection methods and grounding techniques (RAG grounding, output validation, confidence scoring)
- Identify AWS services for governance and regulation compliance (AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, Trusted Advisor)
- Describe data governance strategies (data lifecycles, residency, retention, monitoring) and governance frameworks (e.g. Generative AI Security Scoping Matrix)
Course Info
Promotion Code
Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
TBD
Hardware: Window or Mac Laptops
Job Roles
Job Roles
- AI Engineer
- Machine Learning Engineer
- Data Scientist
- Cloud Solutions Architect
- AI Solutions Architect
- Data Analyst
- AWS Cloud Practitioner
- AI Consultant
- Machine Learning Specialist
- AI Research Scientist
- Cloud Developer
- Data Engineer
- AI Product Manager
- Software Developer
- IT Consultant
- Technical Support Engineer
- Business Intelligence Developer
- AI System Developer
- Cloud Infrastructure Engineer
- Technology Analyst
Trainers
Trainers
Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.
Review
Customer Reviews (8)
- Knowledgeable trainer Review by Course Participant/Trainee
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Very engaging session. The trainer patiently answered all our questions and gave useful tips. (Posted on 17/04/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Knowledgeable trainer Review by Course Participant/Trainee
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Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 20/01/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Learned a lot Review by Course Participant/Trainee
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Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 13/01/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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