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PATH OVERVIEW
Build an AI Operations Copilot - not another toy chatbot.
A product-driven path from programming foundations to reliable AI systems engineering, built around one evolving AI operations command center.
WHO THIS PATH IS FOR
What you'll build
Build an AI operations command center that can process messy business inputs, retrieve relevant knowledge, produce grounded suggestions, route uncertain cases to humans, record what happened, survive failures, and eventually run as a deployed system.
The system evolves across the complete curriculum. Each published phase delivers a bounded capability; later capabilities are added only when their curricula are published.
A · Published capability
Python · Git · Testing · FastAPI · Typed APIs · Structured Outputs
Build the first working layer of AI Operations Copilot from a reproducible project environment through deterministic triage, validation, files, packaging, typing, tests, CLI, FastAPI, Pydantic, provider abstraction, and a controlled structured model output.
B · Published capability
SQL · PostgreSQL · SQLAlchemy · Transactions · Alembic · API Persistence
Turn the stateless Smart Inbox Triage service from Phase A into a durable backend with SQL, PostgreSQL, SQLAlchemy, transactions, repositories, migrations, persisted analysis runs, idempotent API behavior, filtering, pagination, indexes, and database integration tests.
C · Published capability
Evals · Confusion Matrix · Precision + Recall · Slice Analysis · Regression Gates · Robustness
PREREQUISITE EXPECTATION
No prior AI engineering experience is required. You need a computer that can run Python and Git, and you must be ready for focused implementation, testing, debugging, and written reflection.
Curriculum
Foundations
Build the first working layer of AI Operations Copilot from a reproducible project environment through deterministic triage, validation, files, packaging, typing, tests, CLI, FastAPI, Pydantic, provider abstraction, and a controlled structured model output.
14 units
Free preview
A01 · Foundations
A02 · Foundations
A03 · Foundations
Starter path
Start with three complete units from AI Engineering Foundations and use the same learning system as the full course. No account or credit card is required to begin.
Environment, terminal, Git, and the first running component
A01
Python data, collections, and inbound-message representation
A02
Conditionals, loops, and the deterministic triage baseline
A03
METHODOLOGY
TRUST & METHODOLOGY
This path maps authored curriculum skills to a versioned role snapshot. Coverage is not a job-readiness score, hiring promise, certification, or substitute for demonstrated work.
Course outcomes come from the projects, tasks, and checks learners actually complete.
Practical gates and completion are learner-controlled. Quick Checks are formative and the product does not fabricate competence claims.
Course completion is not a hiring promise, certification, rating, or substitute for demonstrated work.
ACCESS
FREE PREVIEW
The public roadmap and complete preview units from the real course.
Start freeFULL PATH
One-time access to every published unit plus progress, Quick Checks, lesson notes, and backup tools.
Choose one course or the four-course All-Access bundle.
Compare access optionsTurn Persistent Operations Memory into an AI Quality Lab: define evaluation contracts, review a curated benchmark, freeze a holdout split, build a local evaluation harness, calculate classification and human-review metrics, analyze slices and failure modes, measure selective automation and execution reliability, compare one controlled candidate, test robustness, and make a release decision without moving the goalposts.
D · Published capability
State Machines · Human-in-the-Loop · Optimistic Concurrency · Idempotent Actions · HTTP Integrations · Audit History
Turn the evaluated AI Operations Copilot into an operational workflow system with explicit state transitions, durable workflow history, human-review queues, approvals and overrides, optimistic concurrency, deterministic action planning, replaceable action adapters, idempotent HTTP execution, bounded retries, reviewer-feedback candidates, and end-to-end workflow metrics.
E · Published capability
RAG · PostgreSQL FTS · pgvector · Hybrid Retrieval · Grounded Citations · AI Security
Extend AI Operations Copilot with a safe and measurable knowledge layer: versioned document ingestion, deterministic chunking, PostgreSQL full-text retrieval, embeddings with pgvector, hybrid ranking, grounded answer drafts, citations, abstention, prompt-injection defenses, privacy controls, bounded failure handling, and separate retrieval, answer, security, and reliability release gates.
F · Published capability
Structured Logging · OpenTelemetry · Docker · GitHub Actions · Health + Readiness · Deployment
Turn AI Operations Copilot into a reproducible deployed portfolio system with release acceptance, privacy-safe structured logs, OpenTelemetry traces and metrics, health and readiness checks, Docker, PostgreSQL-backed integration tests, GitHub Actions CI, a thin operator control room, a restricted public demo surface, deployment smoke tests, and a final technical release gate.
OUTCOMES
OUTCOME 01
Grow AI Operations Copilot phase by phase from a deterministic baseline toward a reliable deployed AI operations system.
OUTCOME 02
Build Smart Inbox Triage in Phase A with validated business messages, explicit triage rules, a CLI, and a typed FastAPI boundary.
OUTCOME 03
Structure, validate, type-check, package, and test Python systems across explicit boundaries.
OUTCOME 04
Isolate model providers and produce schema-constrained suggestions without making the model the architecture.
Persistence
Turn the stateless Smart Inbox Triage service from Phase A into a durable backend with SQL, PostgreSQL, SQLAlchemy, transactions, repositories, migrations, persisted analysis runs, idempotent API behavior, filtering, pagination, indexes, and database integration tests.
11 units
Evaluation
Turn Persistent Operations Memory into an AI Quality Lab: define evaluation contracts, review a curated benchmark, freeze a holdout split, build a local evaluation harness, calculate classification and human-review metrics, analyze slices and failure modes, measure selective automation and execution reliability, compare one controlled candidate, test robustness, and make a release decision without moving the goalposts.
10 units
Workflows
Turn the evaluated AI Operations Copilot into an operational workflow system with explicit state transitions, durable workflow history, human-review queues, approvals and overrides, optimistic concurrency, deterministic action planning, replaceable action adapters, idempotent HTTP execution, bounded retries, reviewer-feedback candidates, and end-to-end workflow metrics.
11 units
Grounded Knowledge
Extend AI Operations Copilot with a safe and measurable knowledge layer: versioned document ingestion, deterministic chunking, PostgreSQL full-text retrieval, embeddings with pgvector, hybrid ranking, grounded answer drafts, citations, abstention, prompt-injection defenses, privacy controls, bounded failure handling, and separate retrieval, answer, security, and reliability release gates.
9 units
Production
Turn AI Operations Copilot into a reproducible deployed portfolio system with release acceptance, privacy-safe structured logs, OpenTelemetry traces and metrics, health and readiness checks, Docker, PostgreSQL-backed integration tests, GitHub Actions CI, a thin operator control room, a restricted public demo surface, deployment smoke tests, and a final technical release gate.
9 units