Course Content
ML Design Basics
This topics covers the fundamentals of Machine Learning System Design in real world.
0/11
Requirements and Business objectives
Data Basics
Training Data
Feature Engineering
Feature Engineering II
Model Development and offline evaluation
Model deployment
Monitoring
Test in production
Infrastructure and tooling
User Experience
Interview Expectations
This topic talks about the expectations and focus areas in the interview and shares some insights into what kind of signals the interviewer captures during the interview.
0/7
Introduction
Problem Navigation
Training Data
Feature Engineering
Model Development
Model Evaluation
Model Deployment
Questions
This topics goes through some example questions and how to approach them.
0/5
Restaurant recommender
YouTube recommendations
ChatGPT
Bad advertisement content
Facebook news feed
Machine Learning System Design
About Lesson

This is another section, which I have seen candidates either mention briefly or skip completely.

Here are the list of expectations in this section:

  • Performance evaluation steps in a deployed model. 
  • Monitoring and alerting. 
  • Model Debugging. 
  • How to handle failure?

Successful candidates propose a thorough deployment solution, talk about model debugging strategies, how to monitor the deployed system and ways to handle failures.

Previous
Next
Scroll to Top