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

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Senior Machine Learning Engineer at Cruise

Machine Learning System Design


# 3. Model

## Feature engineering
-  Geolocation of listing (latitude/longitude): Taking raw latitude and raw longitude features is very tough to model as feature distribution is not smooth. One way around this is to take a log of distance from the center of the map for latitude and longitude separately. 


- Favourite place: store user's favorite neighborhood place in 2 dimensions grid. For example, users add Pier 39 as their favorite place, we encode this place into a specific cell, then use embedding before training/serving. 


|**Features**   |**Feature engineering**   |**Description**   |
|:-: |:-: |:-: |
|Listing ID   |Listing ID embedding   |See Embedding in Machine Learning Primer: Feature Selection and Feature engineering.  |
|Listing feature   | Number of bedrooms, list of amenities, listing city    |   |
|Location   | Measure lat/long from the center of the user map, then normalize   |   |
|Historical search query   | Text embedding   |   |
|User associated features: age, gender   | Normalization or Standardization   |   |
|Number of previous bookings   | Normalization or Standardization   |   |
|Previous length of stays   | Normalization or Standardization   |   |
|Time related features   | Month, weekofyear, holiday, dayofweek, hourofday    |   |


## Training data

- User search history, view history, and bookings. We can start by selecting a period of data: last month, last 6 months, etc., to find the balance between training time and model accuracy. 

> In practice, we decide the length of training data by running multiple experiments. Each experiment will pick a certain time period to train data. We then compare model accuracy and training time across different experimentations.


## Model architecture










Learn about the booking model for the Airbnb Rental Search.

Machine Learning Primer

Video Recommendation

Feed Ranking

Ad Click Prediction

Rental Search Ranking

Estimate Food Delivery Time

Conclusion

Machine Learning Knowledge

Machine Learning Model Diagnosis

Booking Model

3. Model

Feature engineering