Machine Learning System Design: Building Scalable AI Solutions

This is help to Learn how to approach machine learning system design.

We will learn how to approach machine learning system design from a top-down design approach

Overview:

Learn how to approach machine learning system design.

Most major companies, such as META, Google, Amazon, etc., expect their ML developers to have a solid engineering foundation and know about the standard development cycle of ML.

Module Objectives:

Learn how to approach machine learning system design.

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

Learn about the problem statement and metrics for building a feed ranking system.

​​Learn about the problem statement and metrics for building an ad click prediction machine learning system.

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.

Content:

1. Machine Learning Primer

Introduction

Feature Selection and Feature Engineering