Turning product policy into models

While the rest of the industry chases massive models, LinkedIn quietly achieved a major engineering breakthrough by going small.

In this episode of Beyond the Pilot, Erran Berger (VP of Product Engineering, LinkedIn) opens the "cookbook" on how they distilled massive 7B parameter models down to ultra-efficient 600M parameter "student" models—scaling AI to 1.2 billion users without breaking the bank.

AI Gets Real Here. This isn't theory. Erran details the exact architecture, the "Multi-Teacher" distillation process, and the organizational shift that forced Product Managers to write evals instead of specs.

In this episode, we cover:

• The Distillation Pipeline: How to train a 7B "Teacher" and distill it to a 1.7B intermediate and 0.6B "Student" for production.

• Synthetic Data Strategy: Using GPT-4 to generate the "Golden Dataset" for training.

• Multi-Teacher Architecture: Why they separated "Product Policy" and "Click Prediction" into different teacher models to solve alignment issues.

• 10x Efficiency Hacks: Specific techniques (Pruning, Quantization, Context Compression) that slashed latency.

• Org Design: Why the "Eval First" culture is the new requirement for AI engineering teams.

🚀 CHAPTERS

00:00 - Intro: LinkedIn's Massive "Small Model" Feat

04:00 - Why Commercial Models Failed at LinkedIn Scale

08:00 - The "Product Policy" Funnel & Synthetic Data Generation

12:00 - The Pipeline: 7B → 1.7B → 600M Parameters

19:00 - The "Multi-Teacher" Breakthrough (Relevance vs. Clicks)

23:00 - How They Achieved 10x Latency Reduction (Pruning/Compression)

31:00 - Changing the Culture: Why PMs Must Write Evals

35:00 - The "Bright Green Matrix": Measuring Success & Future Roadmap

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more at outshift.cisco.com.

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