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Introduction: ML Systems Foundations
On This Page
What This Module Covers
The Lessons
Two Terms Used Throughout
Before You Start
What This Module Covers
Every predictive ML system in every interview is built from the same five pieces. It does not matter whether it ranks videos, catches fraud, or predicts delivery times.
The five pieces are a training pipeline, a feature layer, an inference path, a serving platform, and a monitoring loop. Inference, wherever you meet the word, 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, meaning it is not tied to any single product.
No full designs yet. Just the parts that the next module's designs, like recommendations, feed ranking, and fraud, will assemble. Module 1 gave you the overall picture. This module gives you the individual parts.
The recurring theme is one divide: offline versus online. Training is offline. Big data, big compute, no latency pressure, running on a schedule. Serving is online. One request, tight latency, high availability.
Almost every hard problem in ML systems traces back to this divide. Training and serving need the same data and the same logic, but they work under opposite constraints.
Feature skew, staleness, drift, and cost are all the same tension in different forms. Keep the divide in mind through every lesson. Interviewers probe it constantly.
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 |
Two Terms Used Throughout
Feature. A feature is 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 is in the features. So are most of its bugs.
Label. A label is 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.
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, meaning the actual delivery time, become known? How would it get back to the model?
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On This Page
What This Module Covers
The Lessons
Two Terms Used Throughout
Before You Start