System design is an important component of any ML interview. Being able to efficiently solve open-ended machine learning problems is a key skill that can set you apart from other engineers and increase the level of seniority at which you’re hired.

This course helps you build that skill, and goes over some of the most popularly asked interview problems at big tech companies. You’ll walk step-by-step through solving these problems, focusing in particular on how to design machine learning systems rather than just answering trivia-style questions. 

Once you’re done with the course, you’ll be able to not just ace the machine learning interview at any tech company, but impress them with your ability to think about systems at a high level. If you have a machine learning or system design interview coming up, you’ll find the course tremendously valuable.

Grokking the Machine Learning Interview

It makes sense to consider generating the best recommendation from a large corpus of movies, as a multi-stage ranking problem. Let's see why.

We have a huge number of movies to choose from. Also, we require complex models to make great, personalized recommendations. However, if we try to run a complex model on the whole corpus, it would be inefficient in terms of execution time and computing resources usage. 

Therefore, we split the recommendation task into two stages.  
 
- Stage 1: Candidate generation
- Stage 2: Ranking of generated candidates

Stage 1 uses a simpler mechanism to sift through the entire corpus for possible recommendations. Stage 2 uses complex strategies only on the candidates given by stage 1 to come up with personalized recommendations. 

Let's take a look at the architectural components of the recommendation system.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Architectural Components