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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