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Introduction: ML Systems Foundations
On This Page
- What This Module Covers
- The Lessons
- Two Terms Used Throughout
- Before You Start
1. What This Module Covers
Every predictive ML system in every interview is built from the same five pieces. It does not matter if it ranks videos, catches fraud, or predicts delivery times. The pieces are a training pipeline, a feature layer, an inference path, a serving platform, and a monitoring loop. (Inference, wherever you meet the word, just means running the model to produce a prediction.) Learn the pieces and you can design a system you have never seen before. The pieces do not change. Only the data and the metric do.
This module teaches the pieces. It is deliberately product-agnostic: it is not tied to any single product. No full designs yet. Just the machinery that the next module's designs (recommendations, feed ranking, fraud) will snap together. If Module 1 gave you the map, this module gives you the parts catalog.
The recurring theme is a single divide: offline versus online. Training is offline. Big data, big compute, no latency pressure, runs on a schedule. Serving is online. One request, tight latency, high availability. Almost every hard problem in ML systems traces back to this divide. Here is why: training and serving need the same data and the same logic, but they live under opposite constraints. Feature skew, staleness, drift, and cost are all just that one tension showing up in different clothes. Keep the divide in view through every lesson. Interviewers probe it constantly.
2. The Lessons
| Question the lesson answers | Lesson |
|---|---|
| Why are training and serving separate systems, and what talks across the gap? | Training vs Serving |
| Where do labels and training data actually come from, and who pays for them? | Where Training Data Comes From |
| What are features, and why do they need their own infrastructure? | Features and Feature Stores |
| Should predictions be precomputed or computed on demand? | Batch vs Online Inference |
| How do ids become vectors, and why does retrieval run in milliseconds? | Embeddings and Two-Tower Retrieval |
| How does a model actually run in production, and how do new versions ship? | Model Serving Basics |
| Which offline metric fits which problem, and when must scores be probabilities? | Offline Evaluation: Choosing the Metric |
| How does the system notice it is getting worse, and what does it do about it? | Monitoring, Drift, and Retraining |
| How do you prove a new model is actually better, and launch it safely? | Online Experiments and A/B Testing |
| Can I assemble all five pieces for a real product? | Capstone: An ML Platform for ETA Prediction |
3. Two Terms Used Throughout
- Feature. An input signal the model uses to make a prediction, computed from raw data. "Number of orders this restaurant received in the last 30 minutes" is a feature. The raw order events it is computed from are not. Most of the engineering in predictive ML lives in features. So do most of its bugs.
- Label. The ground-truth outcome the model learns to predict. The delivery took 34 minutes. The transaction was charged back. The user clicked. Training pairs features with labels. Serving has features only, because the label has not happened yet. Where labels come from, and how late they arrive, shapes the whole pipeline.
4. Before You Start
Keep the food-delivery ETA problem from the Module 1 capstone in mind, and sketch rough answers:
- Name five signals you would want when predicting how long a delivery will take, the moment a customer opens the checkout screen.
- Which of those five change second by second, and which change slowly or never?
- When does the ground truth (the actual delivery time) become known, and how would it find its way back to the model?
On This Page
- What This Module Covers
- The Lessons
- Two Terms Used Throughout
- Before You Start