Opening the Tomb: Who’s Really Buried Inside ChatGPT?
Who really built ChatGPT? Explore 10 foundational AI papers, from backpropagation and Transformers to GPT and RLHF, that shaped modern LLMs.
Real-World Practices 🏗️ |
In this section, we move from theory to practical delivery, where Data & AI gradually transform into real-world solutions that teams can rely on in their daily work. We focus on how to build, deploy, and scale robust products that consistently deliver measurable business impact. From scalable pipelines to advanced analytics, and generative copilots, you’ll find concrete actionable insights, hands-on tutorials, and valuable lessons learned directly from the field.
What You’ll Find Here
- ⚙️ Principles & Patterns: Explore the guiding principles, reusable patterns, and mental frameworks that help distinguish artifacts from true products.
- 📊 Showcases: See real examples of data & AI products in action: dashboards, predictive engines, and generative assistants.
- 📖 Case Studies: Explore real-world initiatives, what worked, what went wrong, why it happened, and the lessons learned.
Mapping the Landscape of Data & AI Products
The diagram shows three product families: Analytics/BI to describe the past, Predictive/Prescriptive to anticipate outcomes, and Generative/Agentic to create and act.
These rely on enablers: Data as a Product for trust and usability, and Data Platforms for storage, transformation, and scale.
