Gain insights into ML system design, state-of-the-art techniques, and best practices for scalable production. Learn from top researchers and stand out in your next ML interview.

Machine Learning System Design is an important component of any ML interview. The ability to address problems, identify requirements, and discuss tradeoffs helps you stand out among hundreds of other candidates. Readers of this course able to get offers from Snapchat, Facebook, Coupang, Stitchfix and LinkedIn. 

This course will help you understand the state of the practice on model techniques along with best practices in applying ML models in production at scale. Once you finished the course you can learn more use-cases at: http://mlengineer.io/

Once you're done with the course, you will be able to apply and leverage knowledge from top researchers at tech companies. You will have up to date knowledge in model techniques from hundreds of the latest research and industry papers. There is even a chance that the interviewers will be surprised at the depth of your knowledge.

I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews

Senior Data Scientist at Amazon

I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!

Data Scientist at NVIDIA

I really like what you've built, it'll help a lot of engineers.

MLE at Facebook

It's well organized and the illustrations are well done. Being able to visualize and walk through the steps in order is really helpful in system design. The hints for quizzes is a nice addition, a hint is given if you get stuck in a real interview, mimicking how a real interview 

DS at Fortune 500

Andrew

I just heard back from the recruiter that I passed the Google L5 HC. Thank you very much for sharing the resources on GitHub and for the course on educative.io!

Google Machine Learning Engineer, L5

I got the offer from Intuit. Thanks so much, it would not have been possible without your help.

Senior Machine Learning Engineer, Intuit

I got Google, Facebook, Apple, Tesla, Cruise offer for Senior ML engineer. I thought the course is super helpful. 

Senior Machine Learning Engineer at Cruise

Machine Learning System Design

# Video recommendations
# 1. Problem statement
 Build a video recommendation system for YouTube users. We want to maximize users' engagement and recommend new types of content to users.







# 2. Metrics design and requirements

## Metrics
### Offline metrics 
- Use #key# precision: The fraction of relevant instances among the retrieved instances. #key#, #key# recall: The fraction of the total amount of relevant instances that were actually retrieved. #key#, ranking loss, and logloss. 

### Online metrics
- Use A/B testing to compare Click Through Rates, watch time, and Conversion rates.   


## Requirements
### Training 
- User behavior is generally unpredictable, and videos can become viral during the day. Ideally, we want to train many times during the day to capture temporal changes.

### Inference 
- For every user to visit the homepage, the system will have to recommend 100 videos for them. The latency needs to be under 200ms, ideally sub 100ms.

- For online recommendations, it’s important to find the balance between exploration vs. exploitation. If the model over-exploits historical data, new videos might not get exposed to users. We want to balance between relevancy and fresh new content. 

## Summary

|**Type**|**Desired goals**|
|:-: |:-: |
|Metrics|Reasonable precision, high recall|
|Training|High throughput with the ability to retrain many times per day|
|Inference|Latency from 100ms to 200ms|
||Flexible to control exploration versus exploitation|

Learn about the problem statement and metrics for building a video recommendation system.

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Video recommendations

1. Problem statement