COURSE 17Advanced12 IN-DEPTH LESSONS

Data Systems, MLOps and Production

Operating ML in production: feature stores, training-serving skew, model registries, automated data validation, drift monitoring, and technical debt.

Start Lesson 1: MLOps overview →← All 29 Courses

Course Syllabus & Units

Unit 1

Overview

1 lessons
1.1L2

MLOps overview

**MLOps** is the practice of treating ML like a production system: data, training, deployment, monitoring, and rollback—not a one-off notebook. Google Cloud’s architecture guide defines MLOps as unifying ML *development* and *operations*...

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Unit 2

Training

1 lessons
2.1L2

Distributed training basics

**Distributed training** splits work across accelerators or machines so larger models and batches finish in useful time. In MLOps terms it is one step inside a reproducible training pipeline—not a one-off laptop run. <!-- IMAGE: one GPU ...

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Unit 3

Features

1 lessons
3.1L2

Feature stores

A **feature store** is shared infrastructure for defining, serving, and documenting ML features consistently for training and live inference. Google Cloud’s MLOps level-1 guidance lists a feature store as an optional but powerful compone...

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Unit 4

Monitor

1 lessons
4.1L2

Monitoring and drift

Watching live AI systems for **data drift**, **performance drift**, cost spikes, and safety incidents—then alerting owners with a runbook. Google Cloud’s MLOps guide treats monitoring as the stage that closes the loop: live stats can tri...

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Unit 5

Registry

1 lessons
5.1L2

Model registry and rollback

A **model registry** is the system of record for model versions (and often prompts): what’s live, who approved it, lineage to the training run, and how to roll back fast. MLflow’s Model Registry documents versioning, aliases, tags, linea...

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Unit 6

Skew

1 lessons
6.1L2

Training-serving skew lab

A practice lab to detect when **features differ** between training and live serving—silent accuracy death. Google Cloud’s MLOps level-0 challenges call out handoffs where engineers rebuild features for production APIs, creating skew. Lev...

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Unit 7

Prompts

1 lessons
7.1L2

Prompt versioning in production

Treating prompts like code: versions, reviews, rollbacks, and eval gates—alongside model versions. In LLM products the prompt is part of the “model system,” just as configuration debt is part of classic ML systems (Sculley et al.). <!-- ...

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Unit 8

Mlops Levels

1 lessons
8.1L2

MLOps maturity levels

Google Cloud’s MLOps architecture guide describes **three maturity levels** for how automated your ML delivery is—from fully manual (level 0) to automated training pipelines (level 1) to full CI/CD for those pipelines (level 2). Levels a...

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Unit 9

Pipelines

1 lessons
9.1L2

TFX and Kubeflow pipeline concepts

**Pipelines** turn ML steps into a directed workflow: validate data → transform → train → evaluate → push. **TensorFlow Extended (TFX)** is Google’s production ML pipeline platform for scalable components. **Kubeflow Pipelines (KFP)** ru...

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Unit 10

Tracking

1 lessons
10.1L2

MLflow tracking and registry

**MLflow Tracking** logs parameters, code versions, metrics, and artifacts for each experiment run, with a UI to compare results. The **Model Registry** then manages lifecycle: versions, aliases, tags, lineage, and deployment pointers. T...

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Unit 11

Debt

1 lessons
11.1L2

Hidden technical debt in ML

Sculley et al.’s classic paper **“Hidden Technical Debt in Machine Learning Systems”** (NIPS 2015) argues that ML can ship fast but accrue huge *system-level* maintenance costs—beyond normal code debt. The famous diagram: ML code is a ti...

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Unit 12

Systems

1 lessons
12.1L2

ML systems design (CS329S)

Stanford **CS329S — Machine Learning Systems Design** (Chip Huyen and collaborators) teaches an iterative framework for building ML systems that are deployable, reliable, and scalable. Lecture notes grew into the book *Designing Machine ...

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