Instagram ML Question - Design a Ranking Model (Full Mock Interview with Senior Meta ML Engineer)
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- čas přidán 31. 05. 2024
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In this ML System Design video, we ask a Senior Machine Learning Engineer from Meta to design a ranking and recommendation system for Instagram. He focuses on increasing user engagement by optimizing post suggestions from both friends and content creators, aiming to boost daily active users and session times by implementing AI in clever ways. Our guest explains the model's functional requirements, emphasizing predicting engagement actions like likes, comments, and views. He also covers the importance of MLOps tools for analytics, monitoring, and alerts to ensure the system's effectiveness and reliability.
Chapters (Powered by ChapterMe) -
00:00 - Designing Instagram's Ranking Model
03:24 - ML Model for Instagram Metrics
08:33 - ML Pipeline Nonfunctional Requirements
10:22 - Monetization Through Ads
12:03 - ML Pipeline Stages Overview
19:18 - Pretrained Embeddings for Interaction Analysis
24:04 - Comprehensive Model Pipeline Strategy
31:23 - Collaborative Filtering for Efficient Representation
33:13 - Two-Tower Network for Data Filtering
38:43 - ML Maturity & AUC Curve Analysis
44:58 - Microservices for Continuous Learning and Scaling
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The Meta engg guy is on-point. Every stage of the pipeline/process has so many nuances which could have made this into a 2hr+ video - maybe consider doing a podcast version of this and make a guestlist of viewers who can submit questions? @Exponent
As a current master's student in data science actively job hunting, I must say this mock interview is incredible. Thank you so much, Vicram! Where can I find more of your content?
he's cracked. great job to you both
for the candidate generation, I would propose a funel model in which first I use some simple algorithms like logistic regression or Dtree or ANN which he used to quickly narrow down the search space to 1/1000 and then do more advanced techniques for refining it. I will use 2 tower network for ranking my candidates.
Two tower is for learning the embeddings. During serving we use the learnt embeddings from the two tower to located approximate nearest neighbor to the viewer's embedding. In reality we will have several parallel paths to generate candidates - here we show just one in the interest of time. Some of the candidate generation sources include: collaborative filtering (either two tower or matrix factorization), content filtering (keyword/interest matching), popular feeds, viral feeds, connected content feeds (content from socially connected creators) etc.
The deeper model for ranking typically would use thousands of features (windowed aggregates, embedding aggregates, embeddings from pre-trained text/image/video processing models etc.). These models are compute and memory intensive to run and we want to only run them on a select thousand (or so) items for a specific viewer. This keeps the serving latency low. This deep ranking model typically has multiple heads (multi-task) with several predictors (like, comment, share etc.). These individual predictors are weighted to generate a score. Reverse sorted scores can be used for creating a candidate post-item list to show the viewer.
Does that help clarify?
Very educational! Loved it, Keep on brining more ML interviews.. :)
just out of curiosity, do you think the performance is good enough to pass a senior level MLSD interview?
One comment could be that two tower network should also be categorized as collaborative filtering
can someone explain the label part in the two tower model ?
Thank you so much for this video. I have a question. So once the two-tower model is trained, for candidate generation, the embeddings for items are computed offline, and the user embedding is computed on the go with the user features, and that user embedding is used against the item embedding vectors for kNN. Is that correct? If so, since the output of the two-tower model is binary, where would I be getting the embeddings from? From a layer before the sigmoid?
yes, just apply user tower or item tower.
Great interview. I have always been confused, in an ML System design interview, should we focus on the ML model data/training/eval pipeline more or the inference pipeline( which is ore of a traditional system design) more ??
In an ML system design interview, you typically need to cover the entire process, including the problem statement, data engineering, modeling, and deployment. It's important to address both the data/training/evaluation pipeline and the inference pipeline. To determine where to focus more on, you may take cues from your interviewer or directly ask them for guidance.