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

In the previous lesson, we talked about various applications of *named entity linking* system and how it can be used as a component in bigger tasks/systems such as a virtual assistant system. Therefore, we require metrics that:

1. Compare different entity linking models based on their performance.
  
   *This will be catered to by offline metrics.*

2. Measure the performance of the bigger task when a particular model for entity linking is used.

   *This will be catered to by online metrics.*



Let's go over the metrics to evaluate the performance of the entity linking system.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Metrics