ML Engineering begins when a model stops being a research artifact and becomes a production service with cost, latency, and ownership boundaries.
This chapter builds the map of the ML theme: error metrics, lifecycle, serving, release safety, feature pipelines, and the feedback loop around the model.
For interviews and design reviews, it gives you a way to discuss models in the language of system design rather than only in the language of experiments.
Practical value of this chapter
Карта маршрута
Понять, где заканчивается чистый ML и начинается инженерная работа вокруг модели.
Рамка для интервью
Структурировать ML-ответ вокруг жизненного цикла, сервинга, выпуска и контуров обратной связи.
Платформенный взгляд
Увидеть роль данных, модели, платформы и продукта в одной системе.
Навигация
Быстро выбрать следующие главы: метрики, сервинг, MLOps, ранжирование или оценка риска.
Entry point
Machine Learning System Design
A strong next read after this overview if you want to move quickly into ML System Design in interview terms.
ML Engineering is best read not as “one more list of ML topics,” but as a route from the language of metrics and error costs to the full lifecycle of a model in production. This theme answers a practical question: how does a model become an engineering system with data contracts, release discipline, serving, review cycles, and platform responsibility?
Who this theme is for
People preparing for ML System Design interviews
The key challenge here is not training the model, but explaining error costs, rollout, and the operating loop around the model in system-design terms.
ML engineers taking on production responsibility
This route is about release policy, rollback, feature freshness, latency budgets, and ownership across data, model, platform, and product.
Data and AI engineers in adjacent roles
If you already build data pipelines, AI features, or platform services, this theme helps you see where ML needs a separate execution path, review cycle, and operational discipline.
Two practical reading tracks
Start with interviews
A short route for interview prep: start with the language of metrics, then the lifecycle, then the most practical cases.
Start with platform and operations
The route for treating ML as an engineering system: data, serving, review processes, and platform thinking.
How the theme is organized
Theme language
Metrics such as precision and recall, the cost of errors, and the basic interview frame.
Lifecycle in production
How data, training, release, serving, and the feedback loop connect into one delivery system.
Platform and operations
Data and feature planes, serving contracts, operational reliability, and platform thinking.
Applied decision systems
Where ML architecture meets business policy, latency, review cost, and feedback traps.
Skill matrix
| Chapter | Skill | What it gives you |
|---|---|---|
| Precision and recall basics | metricsthresholds | Builds the base language for error price, thresholds, and segment-level degradation. |
| ML Lifecycle | lifecycleownership | Connects the full delivery contour: from a dataset snapshot to the signal for retraining. |
| Model release | releasecalibration | Shows how to change model behavior safely through replay, shadow mode, canary rollout, and A/B experiments. |
| Serving runtime | servingruntime economics | Covers latency budgets, batching, CPU/GPU routing, fallback, and queueing discipline. |
| Human review and data quality | HITLreview operations | Explains how review queues and error taxonomy become part of the operating model. |
| T-Bank ML platform interview | platformdevex | Adds platform thinking, self-service, and standardization of ML workflows. |
| Ranking and recommendations | rankingfeedback traps | Needed to reason about multi-stage ranking, exploration versus exploitation, and product policy around feeds and lists. |
Easy mistakes to make here
Related materials
- ML Engineering theme - The full route with all chapters and difficulty levels.
- AI Engineering: Designing LLM, Agent, and Copilot Systems - The neighboring theme if you care more about LLM products, agents, and evaluation systems.