Cert Notes/ Commute Study Notes
Roadmap
KOEN
CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
AIF-C01 · FoundationalAI Practitioner - Foundational
  • Week 1
    • 1.The Relationship and Differences Between AI, Machine Learning, and Deep Learning
    • 2.Learning Types: Supervised, Unsupervised, and Reinforcement Learning
    • 3.Problems Where ML Fits vs. Problems Where It Doesn't
    • 4.Key Terms: Model, Training, Inference, Feature, Label, Overfitting
    • 5.Week 1 Wrap-Up: AI/ML Fundamentals Review
  • Week 2
    • 1.ML Lifecycle Overview: One Complete Cycle from Data to Operations
    • 2.Data: Structured vs Unstructured, Data Splitting, and the Power of Quality
    • 3.Model Evaluation Basics: Accuracy, Precision, Recall, and Overfitting/Underfitting
    • 4.The Human Role in ML Development: Labeling, Feedback, and Iterative Improvement
    • 5.Week 2 Summary: ML Lifecycle and Data at a Glance
  • Week 3
    • 1.What Is Generative AI: Difference from Traditional ML, and Foundation Models
    • 2.How LLMs Work: Tokens, Embeddings, Context Window, and Inference
    • 3.Prompt Engineering Basics: Good Prompts, Zero/Few-shot, and Limitations
    • 4.Limitations and Risks of Generative AI: Hallucinations, Bias, Non-determinism, and Appropriate Use Cases
    • 5.Week 3 Comprehensive Review: Wrapping Up Generative AI Fundamentals at a Glance
  • Week 4
    • 1.Amazon Bedrock: Fully Managed Service for Renting Foundation Models
    • 2.Amazon SageMaker: A Platform for Directly Training and Deploying ML Models
    • 3.AWS AI Services (1): Managed APIs Handling Images, Documents, Text, and Speech
    • 4.AWS AI Services (2) + Amazon Q: Chatbots, Search, Recommendations, Forecasting, and Generative Assistant
    • 5.Week 4 Comprehensive Review: Complete Map of AWS AI/ML Services
  • Week 5
    • 1.Principles of Responsible AI: Fairness, Bias, Transparency, Explainability, Robustness, Privacy
    • 2.AWS's Responsible AI Tools: SageMaker Clarify, Model Monitor, Bedrock Guardrails, AI Service Cards
    • 3.AI Security: Least Privilege IAM, Data Encryption, PII Protection, PrivateLink, Shared Responsibility Model
    • 4.Data Governance and Compliance: Data Origin·Quality, Model Governance, Audit·Logging, Legal and Ethical Considerations for Generative AI
    • 5.Week 5 Comprehensive Review: Binding Responsible AI·Security·Governance into One
  • Week 6
    • 1.Domain Review 1: AI/ML Fundamentals + Generative AI Fundamentals: Critical Summary
    • 2.Domain Review 2: Foundation Model Applications (AWS AI Services) Critical Summary
    • 3.Domain Review 3: Responsible AI + Security·Governance Critical Summary
    • 4.Full Mock Exam Pace: Five Domains Comprehensive Questions
    • 5.D-Day Wrap-Up: Exam Structure, Keyword → Service Translation Table, Frequently-Missed Traps
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
← AIF-C01/Week 3/Day 1
AIF-C01· AssociateWeek 3 · Day 1~20 min read

Day 1 - What Is Generative AI: Difference from Traditional ML, and Foundation Models

Through Week 2, we've seen the world of "traditional" machine learning. Collect data, attach labels, train a model to solve one specific problem. Spam or not? Is this photo a cat or dog? What will next month's revenue be? Each model does just one job well. Then at the end of 2022, ChatGPT appeared and the world changed. Writing, coding, drawing, summarizing—one model does dozens of jobs. This is Generative AI.

Generative AI is the largest area in the AIF-C01 exam. Today, we aim not just to memorize terms but to truly understand "what exactly makes Generative AI different from traditional ML?" and what Foundation Models (FM) and Large Language Models (LLM) at their core really are.

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