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Journey to 1000 models: Scaling Instagram’s recommendation system
- Instagram has successfully scaled its recommendation system to include over 1000 ML models while maintaining quality and reliability.
- The algorithm involves multiple layers of ranking funnels, constant experimentation, and a large number of models serving user traffic.
- Challenges in scaling included infra maturity lagging behind, slow model launches, and lack of consistent model quality definitions.
- Solutions implemented included a model registry, automated model launch flow, and defining model stability metrics for all models.
- The model registry streamlined model importance and business function information, improving operational response efficiency.
- Implementing criticality levels based on incident severity and automation tools for launch processes enhanced productivity and reliability.
- Model stability metrics were introduced to measure prediction accuracy, addressing issues faster and leading to higher-quality recommendations.
- Lessons learned included the importance of infra understanding, enabling rapid model iteration, and ensuring reliability considers model quality.
- Building robust tools and processes has not only improved operations but also empowered colleagues to drive innovation and growth.
- The focus on quality and collaboration has been instrumental in Instagram's successful journey of scaling its recommendation system.
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