Understand model techniques along with the best practices in applying ML models in production at scale.

Deep Dive into System Design Interview

Machine learning system design is an important component of any machine learning interview. The ability to address problems, identify requirements, and discuss trade-offs can help us stand out among hundreds of other candidates. This module will discuss model techniques, along with best practices in applying scalable machine learning models in production. Once we're done with this module, we’ll be able to apply and leverage knowledge from top researchers at tech companies. We’ll have an up-to-date knowledge of model techniques from hundreds of the latest research and industry papers. We might even be able to impress interviewers once we’ve gone through the lessons of this module.

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.

Booking Model

3. Model

Feature engineering