🍔🧠 1000+ ML Models: Inside Instagram's Recommendation System Architecture

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Journey to 1000 models: Scaling Instagram's recommendation system 🤖

Instagram scaled its recommendation system to over 1000 ML models while maintaining quality and reliability. This massive undertaking required rethinking their entire ML infrastructure, from model deployment to monitoring. The journey provides valuable insights for teams building large-scale ML systems.

The challenge: Managing thousands of ML models across different surfaces with varying criticality levels while maintaining rapid iteration speed and prediction quality.

Implementation highlights:

1. Model Registry: Built a central source of truth for model metadata, criticality, and business context using Configerator

2. Automated Launch Platform: Developed tools for capacity estimation and traffic shifting to reduce launch time from days to hours

3. Virtual Resource Pools: Implemented team-based quotas to manage capacity fairly and enable parallel launches

4. Model Stability Metrics: Created unified health metrics combining calibration and normalized entropy to detect prediction issues

5. Standardized Monitoring: Automated observability and alerts based on model registry metadata

Results and learnings:

• Increased launch velocity from a few to 10+ launches per week

• Reduced model launch time by 2+ days

• Detected previously hidden model quality issues faster

Creating a robust ML infrastructure isn't just about handling scale, it's about empowering teams to move fast while maintaining quality. Instagram's approach shows that investing in developer experience and automation pays dividends in ML velocity.

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This week’s coding challenge:

This week’s tip:

Use jq's @csv filter to reliably convert JSON arrays to CSV, handling edge cases like nested quotes and commas automatically. This is more robust than manual string manipulation and properly escapes special characters.

Wen?

• API data processing: Converting JSON API responses to CSV for spreadsheet analysis or data pipelines.

• Database exports: Transforming JSON document stores into relational formats for migration or reporting.

• Log analysis: Converting structured JSON logs to CSV for compatibility with traditional analysis tools.

“Any idea, plan, or purpose may be placed in the mind through repetition of thought.”
Napoleon Hill

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