

Two Case Studies: Production ML infrastructure and Recommendation Engines - ML 072
May 18, 2022
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Introduction
00:00 • 2min
How Do You Make a Recommendation System Work?
02:27 • 4min
Is There a Higher Number of Students Than Tutors?
06:00 • 5min
ML Engineering - Is There a Dependant Variable?
11:16 • 4min
How Would You Evaluate Without an AD Test?
14:52 • 4min
Do You Like Prediction Modeling or Experimentation?
18:25 • 3min
Is Hyperbriant Tuning Double Work?
21:23 • 3min
Top End Devs - Case Study Two
24:25 • 5min
The Second Case Study Is Going to Be Similar to Coursera
29:05 • 2min
Coursera - Is There a Way to Sort Out the Best Content?
30:59 • 3min
How to Solve the Cold Start Problem in E-Commerce
34:14 • 3min
Cold Start
37:16 • 3min
Machine Learning for Recommendation Engines
39:54 • 3min
Implicit Ratings Make So Much Sense
42:53 • 4min
Deep Learning and Recommendations on a Website?
47:09 • 2min
The Hidden Cost of Complexity in ML
48:48 • 4min