Your proven path to success in Machine Learning Interviews – developed by FAANG engineers. Unlock ML loops at top companies with a System Design approach.

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

# Problem statement

The interviewer has asked you to design a search relevance system for a search engine.





# Clarifying questions
Let's clarify the problem statement by specifying three aspects:  scope, scale, and personalization.

## Problem scope
The interviewer's question is really broad. Your best bet is to avoid ambiguities and ask the interviewer as many questions as you can. This will narrow down your problem space as you are thinking out loud for the best solution.

So, your first question for the interviewer would be something like the following:

_<center>Is it a general search engine like Google or Bing or a specialized search engine like Amazon products search?</center>_

This scoping down of the problem is critical as you dive into finding the solutions. For the sake of this chapter, we will assume that you are working towards finding relevant results using a general search engine like Google search or Bing search, but the techniques and discussion apply to all search engines. 

Finally, the problem statement can be precisely described as:

_<center> Build a generic search engine that returns relevant results for queries like "Richard Nixon", "Programming languages" etc.</center>_

This will require you to build a machine learning system that provides the most relevant results for a search query by ranking them in order of relevance. Therefore, you will be working on the *search ranking problem*.

> 📝 The approach to handle a specialized search engine will be fairly similar.


Let's look at a problem statement that asks you to design a search relevance system for a search engine.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Problem Statement

Problem statement

Clarifying questions

Problem scope