Posts tagged with mlp

Instagram EngineeringJan 25, 2020

Five things I learned about working on content quality at Instagram

Why it matters: Managing content quality at scale requires balancing real-time signals with static analysis. This approach shows how to operationalize quality metrics and use multi-stage ML pipelines to protect users while maintaining high-performance recommendation systems.

Combined manual labeling with classifier scores to create calibrated metrics for statistically significant A/B testing results.
Developed 'read-path' models that utilize real-time engagement signals like comments and likes to improve detection precision.
Maintained 'write-path' filters at the sourcing level to handle low-prevalence violations and ensure a baseline of benign content.
Implemented a multi-stage pipeline that balances high-precision sourcing filters with fine-tuned ranking models.
Established continuous model performance tracking to identify edge cases and maintain user safety standards.

#mlp #data #security

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Instagram EngineeringNov 26, 2019

Why it matters: This article provides a blueprint for building massive-scale recommendation engines. It demonstrates how custom DSLs and multi-stage filtering balance high-velocity experimentation with the extreme computational efficiency required to serve millions of users in real-time.

Instagram uses a three-stage ranking funnel to filter billions of media items into a personalized feed for each user in real-time.
Engineers developed IGQL, a C++ optimized domain-specific language, to allow for high-level algorithm design with low-latency execution.
The system utilizes 'ig2vec' account embeddings to identify topical similarities based on user interaction sequences, similar to word2vec.
Facebook’s FAISS library is used for efficient nearest-neighbor retrieval across millions of account embeddings.
The infrastructure supports massive scale, processing 65 billion features and making 90 million model predictions every second.

#mlp #data #dist

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Instagram EngineeringNov 8, 2019

10 Questions with Shupin Mao, Well-being tech lead

Why it matters: This interview highlights the intersection of machine learning and social responsibility, demonstrating how engineers balance technical innovation with strict privacy and legal requirements in a high-scale, data-driven environment.

Shupin Mao transitioned from academic coding in C to professional iOS development using Objective-C at Facebook.
The Instagram Well-being team utilizes machine learning models to identify and combat the sale of illegal goods like drugs and firearms.
Instagram's engineering culture emphasizes a data-driven approach, where projects are guided by analytical goals and user feedback.
Teams allocate 20% of their time to address ad-hoc issues, ensuring flexibility and responsiveness to unexpected technical challenges.
Engineers work closely with cross-functional partners, including legal, policy, and privacy experts, to review every product change.
The organization maintains a flat management structure, allowing engineers to take on large scopes of work and communicate directly with leadership.

#culture #mlp #mobile

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