5.1 Methods to Secure AI Systems on AWS
IAM Policies and Roles in AWS
4.4 Transparent and Explainable Models
Understanding Model Transparency Interpretability vs. Explainability Choosing Between Interpretability and Explainability Trade-offs When Choosing a Transparent Model Challenges with AI…
4.3 Challenges and Risks of Generative AI Models
Generative AI Hallucination Copyright and Legal Risks Bias and Discrimination Risks Toxic Content Risks Data Privacy Risks Guardrails in Amazon…
AI Interview Questions for Beginners
What is the relationship between Artificial Intelligence – AI, Machine Learning – ML, Deep Learning, Natural Language processing – NLP,…
4.1 Responsible AI: Ethical and Fair AI Systems
Overview of Responsible AI Responsible AI refers to a set of guidelines and principles that ensure AI systems are: Core…
3.10 Integration of LLMs into Applications
1. Questions for Integration into Applications: 2. Retrieval-Augmented Generation (RAG): 3. Handling Outdated Knowledge: 4. Business Objectives and Application Design:…
3.9 Evaluating Foundation Model Performance
1. Questions to Consider for Model Integration: 2. Inference Challenges: 3. Optimization Techniques: 4. Evaluation Metrics for Generative AI: 5.…
3.8 Preparing Data for Fine-Tuning Foundation Models
1. Preparing Training Data: 2. Fine-Tuning Process: 3. Data Preparation in AWS: 4. Continuous Pre-training:
3.7 Training and Fine-Tuning Foundation Models
1. Key Elements of Training a Foundation Model: 2. Difference Between Pre-training and Fine-tuning: 3. Challenges with Fine-tuning: 4. PEFT…
3.6 Prompt Engineering and Latent Space
1. Latent Space and Language Models: 2. Hallucinations and Model Limitations: 3. Key Techniques in Prompt Engineering: 4. Prompt Engineering…