A New Paradigm: AI Engineering
Explore the fundamental shift from traditional model-centric development to a modern, product-centric approach. AI Engineering leverages powerful, pre-existing foundation models to build applications faster and more efficiently, focusing on adaptation and integration rather than creation from scratch.
Traditional ML Engineering
A linear, model-centric process starting with extensive data collection and annotation. The focus is on building a bespoke model from the ground up, which is often slow and expensive.
Modern AI Engineering
An iterative, product-centric cycle that starts with an existing foundation model. The focus is on rapid prototyping, evaluation, and adaptation to solve a specific problem.
Core Concepts Dashboard
Foundation models are the engines of modern AI. This section provides an interactive exploration of their architecture, how they are aligned with human intent, and how their generative capabilities can be controlled. Click the tabs to dive into each concept.
Inside the Transformer
The Transformer architecture is the core of most foundation models. It processes input in parallel using a powerful "self-attention" mechanism. Hover over or tap the components below to learn more about each step.
The AI Development Lifecycle
Building a production-grade AI application is an iterative journey. This interactive diagram outlines the key stages. Click on any node to explore the techniques and considerations for that phase. **Evaluation** is central, influencing every other stage of development.
Production & Optimization
Deploying an AI application is a complex engineering challenge. The goal is to build a system that is performant, cost-effective, and reliable at scale. This involves a sophisticated architecture and continuous user feedback.
Production-Grade AI System Architecture
All components continuously feed data into a central **Monitoring & Observability** system, which in turn informs the **User Feedback Loop**, driving the next cycle of iterative improvement.