Ever wondered how Amazon builds generative AI for millions of sellers? Or how Instagram's recommendation feed knows exactly what you want to watch next? In this deep-dive conversation, we sit down with AI/ML leaders from Amazon and Meta to uncover the real strategies behind building AI products at scale.

Anita shares her journey launching Amazon's first AI image generation solution for sellers, while our Meta engineer breaks down distributed inference and how Instagram's recommendation models actually work.

Key Insights:

→ Why you should validate AI ideas with free tools (Midjourney, Canva) BEFORE building

→ The real difference between AI metrics vs. business metrics

→ How to define "quality" when there's no industry benchmark

→ Why giving users full control over prompts is a mistake

→ How Instagram updates its models without losing your preferences

Timeline:

0:00 - Introduction: Building AI products at scale

1:19 - Launching Amazon's first AI image generation tool

2:06 - Balancing innovation with customer problems

3:16 - The problem: Small sellers can't afford graphic designers

4:03 - Real-world example: Food tech & restaurant images

5:07 - Validating AI with prototypes before building

5:24 - KEY INSIGHT: Use Midjourney/Canva to validate first

6:12 - Quality dimensions: Aesthetics, relevance, proportions

8:48 - Product manager's dilemma: AI metrics vs. business metrics

9:30 - Creating benchmarks when none exist

10:30 - Responsible AI: Safety, watermarks, artifacts

11:04 - Business metrics: Adoption, engagement, retention

12:05 - Defining accuracy in generative AI

13:56 - Don't make users prompt engineers (abstract the complexity)

15:26 - Fundamentals of inference explained

16:09 - Training vs. Inference: The dog analogy

17:00 - Why training and inference aren't binary

18:43 - How Meta does distributed inference

19:32 - How Instagram recommendations actually work

20:26 - Snapshot updates: Keeping models fresh

21:01 - Replacing models without losing user context

22:38 - What is distributed inference? Tree structure explained

23:31 - How Instagram serves personalized content at scale

Who This Is For:

Product managers building AI/ML products

Engineers working on generative AI

Startup founders exploring AI solutions

Anyone curious about how Big Tech AI actually works

Resources Mentioned:

Stable Diffusion aesthetic models

Midjourney for prototyping

Canva for quick validation

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