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Reinforcement Learning from Human Feedback (RLHF)

Back to: Mastering AWS Certified AI Practitioner AIF-C01 – Hands On!

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Fine-Tuning Foundation Models

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Preparing Data for Fine Tuning

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Lessons

Introduction
- Introduction
- Get your Copy of the Slides and Study Guide
AI and Machine Learning Fundamentals
- Taxonomy of Artificial Intelligence and Machine Learning Techniques
- Supervised Learning Techniques
- Evaluating Supervised Machine Learning with Train/Test and Cross Validation
- Unsupervised and Self-Supervised Learning; Reinforcement Learning
- The Bias / Variance Tradeoff
- A Taxonomy of Machine Learning Techniques
- Intro to Natural Language Processing (NLP)
- History of Deep Learning; How Neural Networks Work
- Training and Tuning Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- The Transformer Architecture and Self-Attention: How Generative AI Works
- Transformers: Key Terms (temperature, context windows, max tokens, etc.)
- Generative Adversarial Networks (GANs)
- Diffusion Models
- Residual Neural Networks and WaveNet
- Quiz: AI and ML Fundamentals
Machine Learning Design Principles and Use Cases
- Machine Learning Design Principles and Lifecycle
- Business Goal Identification
- Framing the Machine Learning Problem
- Data Processing
- Model Development, Training, and Tuning
- Deployment
- Monitoring
- The AWS Well-Architected Machine Learning (ML) Lens
- Machine Learning Ops (MLOps)
- AI Use Cases
- Computer Vision Use Cases
- Generative AI Use Cases
- Quiz: ML Design Principles and Use Cases
Prompt Engineering
- Benefits of Prompt Engineering
- Anatomy of a Prompt
- Prompt Best Practices
- Types of Prompts
- Avoiding Prompt Mis-Use and Mitigating Bias
- Quiz: Prompt Engineering
AI and Machine Learning with Amazon SageMaker
- Set up an AWS Billing Alarm
- Overview of Amazon SageMaker
- Data Processing, Training, and Deployment with SageMaker
- SageMaker Studio, SageMaker Debugger, SageMaker Experiments
- SageMaker Autopilot
- SageMaker Model Monitor and SageMaker Clarify
- SageMaker Deployment Safeguards (and other features)
- SageMaker Feature Store
- SageMaker Lineage Tracking
- SageMaker Data Wrangler
- SageMaker Canvas
- DEMO: SageMaker Studio, SageMaker Canvas, SageMaker Data Wrangler
- Linear Learner, XGBoost, Seq2Seq
- DeepAR, BlazingText, Obj2Vec, Object Detection
- Image Classification, Semantic Segmentation, Random Cut Forest, NTM, LDA
- KNN, K-Means, PCA, Factorization Machines, IP Insights
- DEMO: Training and Inference with SageMaker and XGBoost
- Quiz: Amazon SageMaker
Generative AI with AWS
- Generative AI with Foundation Models and SageMaker JumpStart
- Introduction to Amazon Bedrock
- HANDS ON with the Bedrock Playground for Chat, Text, and Image Generation
- Fine-Tuning Foundation Models with Bedrock
- Bedrock Knowledge Bases and Retrieval-Augmented Generation (RAG)
- HANDS ON with Bedrock Knowledge Bases
- Bedrock Guardrails
- HANDS ON with Bedrock Guardrails
- LLM Agents and Bedrock Agents
- HANDS ON with Bedrock Agents
- More Bedrock Features
- Bedrock Model Invocation Logging
- Amazon Bedrock Pricing and Security
- Amazon Q Developer (formerly CodeWhisperer)
- Amazon Q Business
- Amazon Q Apps and Pricing
- Amazon Quicksight and Quicksight Q
- Quiz: Generative AI with AWS
High-Level AWS AI Services
- Amazon Comprehend
- Amazon Translate
- Amazon Transcribe
- Amazon Polly
- Amazon Rekognition
- Amazon Forecast
- Amazon Lex
- Amazon Personalize
- Additional AI and ML Services
- Quiz: High-Level AI Services
Training, Tuning, and Measuring your Models
- Fine-Tuning Foundation Models
- Reinforcement Learning from Human Feedback (RLHF)
- Preparing Data for Fine Tuning
- Evaluation Techniques for Foundation Models
- ROUGE, BLEU, and BERTscore metrics for LLM’s
- Choosing a Generative AI Evaluation Strategy
- Machine Learning Model Evaluation: Precision, Recall, F1, RMSE
- ROC Curves, AUC, P-R Curves
- Measuring Regression Models (r-squared, RMSE, MAE)
- Quiz: Training, Tuning, and Measuring your Models
Responsible AI, AI Governance, and AI Security
- Dimensions of Responsible AI, and AWS Tools for Responsible AI
- Best Practices for Responsible AI
- SageMaker Clarify Metrics and Techniques
- AI Governance and Service Cards
- Responsible Model Selection; Responsible Agency
- Responsible Dataset Preparation
- Transparency of AI Models
- AI and Human-Centered Design (HCD)
- Defense In Depth
- Additional AI Security Services
- Security in Data Engineering
- AWS Shared Responsibility Model
- AI Compliance Concerns
- Data Governance
- AWS AI Governance Services
- The Generative AI Security Scoping Matrix
- Quiz: Responsible AI, Security, and Governance
Practice Exam
- Practice Test (20 questions) for AWS Certified AI Practitioner AIF-C01
Wrapping Up
- What to Expect: Exam Tips and Preparation
- Overview of the Exam Guide
- The New Question Types: Ordering, Matching, and Case Study
- Looking Ahead…

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