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

A successful machine learning system should be able to gauge its performance by testing different scenarios. This can lead to more innovations in the model design. For an ML system, “success” can be measured in numerous ways.
Let’s take an example of an advertising platform that uses a machine-learning algorithm to display relevant ads to the user. The success of this system can be measured using the users' engagement rate with the advertisement and the overall revenue generated by the system. Similarly, a search ranking system might take into account correctly ranked search results on SERP as a metric to claim to be a successful search engine.  Let’s assume that the first version of the system (v0.1) has been created and deployed.

# Hypothesis and metrics intuition
At any point in time, the team can have multiple hypotheses that need to be validated via experimentation. 

Imagine, for instance, that the team designing an ad prediction system wants to test the hypothesis that increase in the neural network model depth (increase in hidden layers) or width (increase in activation units) will increase latency and capacity but will still have an overall positive effect on user engagement and net ad revenue.




Similarly, a team working on designing a search engine wants to test the hypothesis that the pointwise algorithm instead of the pairwise algorithm would positively impact search relevance.



Irrespective of the problem you're working on, model experimentation and evaluation flow are always critical. In this lesson, we will go over the key steps and concepts in model experimentation and evaluation.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Online Experimentation

Hypothesis and metrics intuition