Instagram ML Question - Design a Ranking Model (Full Mock Interview with Senior Meta ML Engineer)

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Instagram ML Question - Design a Ranking Model (Full Mock Interview with Senior Meta ML Engineer)

Рет қаралды 26,879

Exponent

Күн бұрын

Пікірлер: 22

@MeghavVerma22 6 ай бұрын

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

@mulangonando2942 Ай бұрын

This is perfect content for even guys who are just looking to activate mental faculty in fullstack ML design. The whole scale of thought process from concept to concrete algorithms is super transparent for many

@pranav7471 2 ай бұрын

The best ML design interview I have seen so far!

@maryamaghili1148 4 ай бұрын

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.

@mullachv 4 ай бұрын

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?

@Sean-ke9ii 8 күн бұрын

ALS can be replaced with gradient descent for collaborative filtering. If you are doing gradients for a more complicated neural network, then surely you can follow this approach for a simpler bilinear model. Thanks for the content.

@soustitrejawad 5 ай бұрын

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?

@tryexponent 2 ай бұрын

Thanks for your feedback! You can check out the full interview course at bit.ly/4bUEPbF to see more of Vikram's mock interviews.

@_anarki_ 3 ай бұрын

he's cracked. great job to you both

@abhipatel9048 6 ай бұрын

Very educational! Loved it, Keep on brining more ML interviews.. :)

@xgu185 5 ай бұрын

One comment could be that two tower network should also be categorized as collaborative filtering

@RezaE 2 ай бұрын

This was a great mock interview. Thanks for sharing it.

@EranM Ай бұрын

Where is the cycle of learning? How about monitoring? When do we train? Do we automate it? how?

@haoyuwang3243 3 ай бұрын

just out of curiosity, do you think the performance is good enough to pass a senior level MLSD interview?

@MrAnujchopra Ай бұрын

As I understand, it is just 1 model for candidate selection and for ranking. Then why go to that same model twice? We generate post embedding asynchronously. Does Approximate Nearest Neighbour search is faster than taking dot product with all items. Also 0.5 ROC-AUC is not random prediction but a constant prediction for all values.

@alexilaiho6441 3 ай бұрын

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 ??

@tryexponent 3 ай бұрын

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.

@PrudhviRaj12 5 ай бұрын

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?

@chun5919 4 ай бұрын

yes, just apply user tower or item tower.