Module 01: Systems Foundations
Every production AI failure traces back to a decision made early in the design process - a requirement misunderstood, a constraint overlooked, a scaling assumption that held at 1,000 users and collapsed at 1,000,000. This module builds the mental frameworks that prevent those failures.
What You Will Learn
Systems thinking for AI is different from systems thinking for traditional software. A web server either returns a response or it doesn't. A model returns a prediction - but the prediction might be stale, wrong, biased, or technically correct but business-irrelevant. Understanding that difference is the foundation of everything in this track.
Module Roadmap
Lessons at a Glance
| # | Lesson | Core Skill |
|---|---|---|
| 01 | ML System Design Framework | Structure any AI design problem in 45 minutes |
| 02 | Requirements and Constraints | Translate vague asks into measurable specs |
| 03 | Back-of-Envelope Estimation | Size traffic, storage, and compute from first principles |
| 04 | Latency vs Throughput | Diagnose and fix tail latency in ML serving |
| 05 | Consistency and Availability in ML | Apply CAP theorem to feature stores and serving |
| 06 | Data Systems for ML | Choose the right data architecture for your use case |
Key Themes
Systems before algorithms. The most common failure mode in ML engineering is jumping to model architecture before understanding system constraints. Every lesson in this module is designed to build the habit of thinking about the system first.
Scale changes everything. A design that works at 1,000 requests per day breaks at 1 million. The estimation and trade-off lessons train you to reason about scale before you write a line of code.
ML introduces new failure modes. Traditional distributed systems have consistency, availability, and partition tolerance to worry about. ML systems add training-serving skew, data drift, model staleness, and feature pipeline failures. This module introduces all of them.
Prerequisites
- Basic understanding of distributed systems concepts (databases, APIs, caching)
- Familiarity with Python and basic ML concepts
- No prior experience with large-scale ML infrastructure required