How to Design Machine Learning Systems (Beginner to Pro)
Stop thinking like a student and start thinking like an ML Architect.
In this video, we dive deep into Chapter 2 of ML Systems Design: Moving from model-centric thinking to system-centric thinking. Most ML courses teach you how to minimize a loss function, but they don't teach you how to move a business metric.
What you’ll learn:
The Golden Rule: Why companies don't care about your accuracy if it doesn't lead to revenue or retention.
The 4 Pillars of Production: Reliability, Scalability, Maintainability, and Adaptability.
Reframing Problems: How to turn a vague "customer service is slow" …
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Chapters (7)
The Beginner vs. Pro Mindset
0:45
Business Objectives vs. ML Metrics
2:30
The 4 Pillars of a Production System
4:15
The ML Life Cycle (It’s a loop, not a line)
6:00
Framing the Problem: Classification vs. Regression
8:30
Why Data always beats the "Mind"
10:15
Summary & Checklist
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