The ML Engineer's Path #4advanced

ML Engineering for Principal Engineers

You don't need to train models — you need to design the systems that train, serve, monitor, and scale them. GPU infrastructure, cost optimization, model serving patterns, ML CI/CD, security, team topology, and incident response. The system design interview for ML, but practical.

What You Build

Complete ML system design: architecture document with training infra, serving, monitoring, cost model, team structure, and rollout plan

Sections

Lessons

10h

Estimated

Concepts

Prerequisites

PyTorch from Zero to Production

ml-engineeringsystem-designinfrastructuremlopsgpucost-optimizationprincipal-engineer

Dr. Maya Chen

lecture · formal

First 4 lessons are free (22% of the course). No login required.

The Principal Engineer's ML Mandate

Why senior engineers need ML system design skills now.

The Principal Engineer's ML Responsibility

25 min Build in Public

Tweet: Tweet: 'Principal engineers in 2026 need ML system design skills. Starting the deep dive.'

ML System Design vs Software System Design

25 min

Training & Data Infrastructure

GPU clusters, data pipelines, feature stores, experiment tracking.

Training Infrastructure: GPU Clusters, FSDP, DeepSpeed

40 min Ship

Ship: Distributed training config running on multi-GPU

Data Pipelines at Scale: Petabyte ETL

35 min Ship

Ship: Data pipeline processing 1M+ records with validation

Preview — content blurs halfway through

Feature Stores: Feast, Tecton, Hopsworks

30 min

Experiment Tracking at Scale

25 min

Unlock the rest of this course

$5 for full course or $25/mo for everything

See pricing

Production Serving & Cost

Serving patterns, GPU cost optimization, monitoring, A/B testing.

Model Serving Patterns: Sync, Async, Batch, Streaming

35 min

GPU Cost Optimization: Spot Instances, Quantization, Distillation

35 min

Model Monitoring: Drift, Latency, Accuracy Decay

30 min

A/B Testing ML Models in Production

30 min

ML Platform Engineering

Platform architecture, CI/CD for ML, security, LLM patterns.

ML Platform Architecture: Build vs Buy

35 min

CI/CD for ML: Testing Models, Not Just Code

35 min

Security & Compliance: Model Cards, Bias Audits, PII

30 min

LLM Integration Patterns: RAG, Agents, Fine-Tuning Decisions

35 min

Organization & Operations

Capacity planning, team topology, incident response.

Capacity Planning: GPU Budgets, Scaling Curves, TCO

30 min

Team Topology: ML Engineers, Data Scientists, Platform Engineers

25 min

Incident Response for ML: When Models Fail in Production

25 min

Final Ship

Design a complete ML system for your organization.

Design an ML System for Your Organization

60 min

Next Course

LLM Orchestration & RAG Systems

The skills every AI engineer needs: retrieval-augmented generation, vector databases, embedding pipelines, multi-step agents, and function calling. Build a production RAG app from scratch with evaluation and monitoring.

ML Engineering for Principal Engineers

What You Build

Complete ML system design: architecture document with training infra, serving, monitoring, cost model, team structure, and rollout plan