Life of a Model: Build, Teach, Tune, Ship
Follow an AI models journey from text to real users in four FREE lessons: 1. Build a model (pre-training) 2. Teach a model (post-training) 3. Tune a model (fine-tuning, distillation, quantization) 4. Ship a model to production (evals, inference, deployment)
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Tue Nov 3·6:30 PM UTC
🏗️ Build a Model: Pre-Training and GPU Clusters
Every AI assistant you use starts as a base model trained to predict the next token. Understanding pre-training explains what LLMs know, why they hallucinate, and why frontier models cost hundreds of millions to build. If you make decisions about AI products, budgets, or vendors, this is the foundation. A base model knows a lot but doesn't know how to behave, and that gap drives everything that comes next.
You'll learn from
Priyanka Vergadia
Visual Product Storyteller | Built & Shipped AI at Google & Microsoft
Thu Nov 5·7:00 PM UTC
🧑🏫 Teach a Model: Post-Training, RLHF, and Reasoning
A base model can complete text, but it can't reliably answer a question, refuse a harmful request, or work through a hard problem. Post-training is where those behaviors are shaped, and it's why ChatGPT, Claude, and Gemini feel so different from raw models. It's also where quality and safety trade-offs get decided, which makes it essential knowledge for anyone judging or choosing a model.
You'll learn from
Priyanka Vergadia
Visual Product Storyteller | Built & Shipped AI at Google & Microsoft
Mon Nov 9·7:00 PM UTC
⚙️ Tailor a Model: Fine-Tuning, LoRA, and Quantization
Most teams never train a model from scratch, but almost every team needs to adapt one and run it affordably. Fine-tuning fits the model to your task, while distillation and quantization fit it to your budget. These choices decide whether an AI feature stays a demo or becomes a product you can serve at a cost that makes business sense.
You'll learn from
Priyanka Vergadia
Visual Product Storyteller | Built & Shipped AI at Google & Microsoft
Wed Nov 11·7:00 PM UTC
🚀 Launch a Model: Evals, Inference, and Deployment
A model isn't done when training ends. Evals tell you if it's good, and inference and deployment decide whether anyone can actually use it. Latency, cost per token, and reliability make or break an AI product in the real world. This lesson closes the loop: production feedback becomes the data that starts the next life of the model.
You'll learn from
Priyanka Vergadia
Visual Product Storyteller | Built & Shipped AI at Google & Microsoft