Netflix ML Question - Design a System to Predict Netflix Watch Times (Full mock interview)
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- Опубліковано 17 чер 2024
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This interview delves into the use of machine learning in predicting Netflix watch time. Our guest covers a wide range of topics, including data utilization, feature engineering, supervised and unsupervised approaches, and similarity metrics. They also discuss the potential for combining these approaches to create a versatile recommendation system. Additionally, the interview explores challenges related to model deployment, temporal effects, and considerations for retraining. This conversation shows the complexities of machine learning for watch time prediction.
Chapters (Powered by ChapterMe) -
00:00 - Intro
00:29 - Design goal Netflix watch time prediction
01:36 - Access to user data demographic, movie, show features
04:47 - Training model for watch time prediction
09:42 - Sparse matrix, similarity search, metrics
13:12 - Pearson correlation for watch time prediction
15:02 - Unsupervised methods offer advantages over supervised
18:24 - Linear model embeddings simplify learning
19:03 - Deployment issues and model integration
23:16 - Matrices for similarity over latent factor models
24:17 - Issues with supervised hot feature vectors
25:22 - Supervised feature-based prediction using embedding space
27:43 - Outro
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Can you ask the interviewee to draw out the system he's talking about, next time? I'm not a machine learning engineer, but in my tech interviews, it's not just verbal, you have to be coding, or system design drawn on miro, coderpad, or some other virtual whiteboard while you share your thought process.
Thanks for positing this vedio. It helped me a lot for ML System Design interview.
Thanks for the video
It would have been great if you had used some form of sketch board.
This is a good example, but the question of why are we predicting watch time - no one asked...
Nice! 👍🏼
Thank you very much for posting the video. This is a perfect way to illustrate how an ML SD interview goes. I really appreciate it. Could I ask how well you feel the answer to the question in the video? Are they the correct or strong answers you expect the interviewees to give?
Hey shuier525, glad this was helpful! This was a strong performance by Nathan 💪. He did well in terms of understanding the problem, understanding the data, engineering the model and finally talking through the considerations of deploying the model; all the while communicating clearly and collaborating well with the interviewer.
We have a much more detailed rubric on our site: www.tryexponent.com/courses/ml-engineer/ml-system-design/mlsd-rubric
So check it out if you are interested!
@@tryexponent Thank you so much for replying to me. Getting to know this feedback is very helpful. Great video by the way.
Thanks for posting the video.
Although I don't think this is a good example of ML Design interview.
1. Interviewees are expected to lead a conversation, rather than answering questions all the time.
2. Interviewees are expected to talk about the trade-off between scalability and model accuracy, but not how one model might perform better than the other.
3. The interviewee spent too much time in stats, which means he won't have time for other topics such as experimentation and serving.
Hey zijiali8349, thanks for taking the time to share your feedback. Appreciate it!