AI Engineering in 76 Minutes (Complete Course)
AI Summary
Overview of AI Engineering by Chip Winn
- Introduction: The video summarizes the book “AI Engineering” which discusses the field of AI engineering, its components, and career opportunities.
Key Concepts Covered
- AI Engineering: Focuses on building applications on top of foundation models rather than creating new models from scratch.
- Foundation Models: Explain how these large models have transformed AI with self-supervised learning and multimodal capabilities.
- Architecture: Discusses the Transformer architecture and how it improves upon traditional neural networks.
- Training Data Challenges: Addresses issues related to data quality and biases in training datasets.
- Evaluation Metrics: Introduces metrics for evaluating AI models and the challenges inherent in this process.
- Model Selection: Advice on choosing the right foundation model based on performance and cost.
- Prompt Engineering: Describes techniques to craft effective prompts for better model responses.
- Retrieval-Augmented Generation (RAG): A method for enhancing model outputs using external data sources.
- Agentic Approach: Details how agents can perform actions based on tasks and make decisions.
- Fine-Tuning: Discusses when and how to fine-tune models for specific tasks.
- Data Centric vs Model Centric: Emphasizes the importance of high-quality data in AI development.
- Inference Optimization: Covers techniques to reduce costs and improve latency in model inference.
- User Feedback: Highlights the value of gathering user feedback for continuous improvement in applications.