MLOps & LLM Fine-Tuning in Practice: Take Models from Lab to Production
Signature Course No. 314 — A Signature AI Socratic Coaching Course.
No experience needed — your coach starts where you are.
A New Kind of Learning
Socratic coaching, delivered by an agentic AI coach.
Not a course you watch. An AI coach you work with.
The Lighthouse Coach begins by discovering how you think about the subject, teaches what is missing, and gives you realistic practice before asking you to prove that you can use it. You may test out of concepts you already understand, while capstones turn the learning into a practical plan, decision, conversation, or other real-world result.
The curriculum is deliberately authored; the AI delivers it through coaching. It is not an AI randomly generating lessons.
A coach that teaches by asking — not by lecturing
The Lighthouse Coach is an AI Socratic coach built on Cognosco Learning's MLOps & LLM Fine-Tuning in Practice: Take Models from Lab to Production course, in the AI at Work category. It does not play videos at you. It holds a conversation: it asks you a question, listens to your answer, names the misconception it hears, affirms what you got right, and asks the next question — the way a great coach works in the room.
Production is not the lab with more users. It is a different problem that happens to share a model: thousands of users, malformed inputs, a latency budget, a monthly bill, and failures that arrive correlated — ten thousand bad cases in an hour, all wearing the same defect. Most language-model features do not die because the model was weak. They die in the gap between the demo and Tuesday: no evaluation worth trusting, no dataset worth fitting on, no gate between a new version and real users, and no plan for the day it drifts.
It starts with the running deployment, in full. Ternlight Systems' Dispatch Helper drafts replies and summaries for freight coordinators — 120,000 calls a month, 900 input tokens and 220 output tokens a call, $0.0060 a call raw and $720 a month, a 2,500-millisecond ninety-fifth-percentile budget, an instruction set of 4,800 examples and a golden set of 400 cases passing 288 — 72 percent — with failures clustered as behavior first and knowledge second. Every cost, evaluation, and gate in the course is computed from those figures, stated once in Concept 1 and never quietly swapped for friendlier ones.
It works in two movements. First, Module 1 — Decide and Build: The Approach, the Data, the Run — work through Concepts 1–9, finishing with The Production Decision Dossier; then, Module 2 — Prove and Operate: Evaluation, Serving, and the Live Feature — work through Concepts 10–18, finishing with The Production Readiness and Operations Plan.
Both capstones produce real artifacts — a production decision dossier for Dispatch Helper: the six operating facts, the first decision with its reversal condition, the data plan, the fitting mandate written as a measurable contract, the method chosen on arithmetic, and the run record; and a production readiness and operations plan: the layered evaluation evidence with its audited judge and calibrated bench, the decomposed tail budget, the whole bill including the success scenario, the gates, monitors, and rollback triggers, the safety layer, and the signed standard.
The honesty standard is part of the course. This course teaches the production discipline around language models, and it states its boundary up front: it does not re-teach retrieval architecture — Signature Course No. 203 owns that subject — or machine-learning engineering foundations, which belong to No. 213. Its prices are the book's stated planning prices for the running deployment, not a claim about any provider's price today; in your own work you substitute your provider's current prices into exactly the same arithmetic and check every figure against Concept 1.
The course is self-paced and always on — roughly 30 minutes per concept, about 9 hours in full, taken in as many sittings as you like. You can type your answers, or talk out loud: the Voice Coach speaks its questions and listens to your spoken replies. Learners who already know the material can test out concept by concept.
MLOps & LLM Fine-Tuning in Practice: Take Models from Lab to Production is part of The Lighthouse Signature Series — the online counterpart to the one-day, in-person learning Cognosco Learning has delivered since 2002.
Course Facts
One full concept, coached end to end — free: verify your email and we’ll send you a link to start.
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Diagnose → Learn & Practice → Prove → Capstone
Every learner moves through the same discipline a Cognosco instructor would insist on in the room.
Diagnose
The Coach starts where the course starts: with the deployment in full. Take a model feature you know and state its six operating facts — traffic, call shape, price per call, latency budget, dataset sizes, and measured pass rate — and name the decision you have been making blind without the one you cannot state; say why the book budgets the tail instead of the average, and whose experience each number describes; name three real costs the $720 raw figure excludes, and the concept that prices each one. Every concept opens with three questions that locate where you actually stand, and your answers shape how deeply the Coach works the concept with you. Everyone begins with Module 1 — no skipping the foundations you only think you have.
Learn & Practice
Work each concept in conversation with the Coach — spoken or written — then practice it against realistic situations until it holds up.
Prove
Every concept ends in proof: answer the check cold and demonstrate the skill. Know it already? Test out and take credit immediately.
Capstone
Finish with two capstones — The Production Decision Dossier for Module 1 and The Production Readiness and Operations Plan for Module 2 — that pull the whole course together, and earn the certificate.
What You'll Learn
Decide on evidence, measure what the business feels, operate what you ship — in that order, every time. Every move in this course is paired with the evidence that move must produce: a golden set built before the change it judges, a judge whose agreement with humans has been counted, a gate with its threshold written down in advance, and a rollback rehearsed before it is needed.
Module 1 decides and builds. Production Is a Different Problem; The First Decision; Prompting First; When Fitting the Model Wins; The Instruction Set; The Golden Set; Splits, Leakage, and Contamination; Full Fitting or Parameter-Efficient; and The Reproducible Run — the approach chosen on a failure map, the data curated as the durable asset, and the run made reproducible.
Module 2 proves and operates. Evaluation Design; The Judge and Its Failure Modes; Human Review That Calibrates; Serving Inside a Latency Budget; Cost Per Call; Monitoring and Drift; Regression Gates, Versioning, and Rollback; Safety in Production; and The Production Standard — the evaluation defended in layers, the feature served and costed whole, and the release made reversible.
And it runs on one set of figures throughout. Ternlight's traffic, call shape, prices, dataset sizes, and pass rate are stated once, in Concept 1, and used in every concept that follows — no second deployment with friendlier numbers, and no worked example that quietly swaps in a different call volume because the multiplication was easier. A course about production discipline that will not submit to its own arithmetic has no business teaching anyone else's.
What you will learn
- Decide between prompting, retrieval, and fine-tuning — and state the evidence that would change the decision
- Curate instruction and evaluation datasets that are clean, split honestly, and free of leakage
- Choose between full and parameter-efficient fitting, and run a job you can reproduce
- Design evaluations — golden sets, automated judges, and human review — whose results you can defend
- Serve a model feature inside a latency budget and cost every call
- Monitor a live feature, detect drift, and gate releases on regression evidence
- Version, roll back, and keep a production feature safe — and sign the standard that proves it
Everything in the course
- All 18 concepts of MLOps & LLM Fine-Tuning in Practice: Take Models from Lab to Production, in 2 modules
- A starting-point check first — the Coach finds where you actually stand before teaching
- Learn, Practice, Prove for every concept — no passive video watching
- A test-out on every concept: prove you know it and move on
- The Voice Coach: talk with the Coach out loud — it speaks, listens, and replies
- A personal Learner Record of your work across the course
- Two capstone exercises: The Production Decision Dossier and The Production Readiness and Operations Plan
- A printable session summary whenever you pause
- The Lighthouse Coach completion certificate (Signature Series)
- Your progress is saved as you go — stop anytime, resume where you left off
Built for the people who own a model feature after the demo
ML and platform engineers — who own a model feature after the demo and need the release, monitoring, and rollback discipline that keeps it alive; and data scientists moving into production work — whose experiments are sound and who need the operational half: datasets as assets, evaluations as gates, versions as contracts.
Product managers for AI features — who must decide where the next dollar goes — a better prompt, a retrieval layer, or a fitting run — and defend that decision with evidence.
Technical leads and founders — shipping a first language-model feature with a small team, no dedicated operations staff, and no room for a public failure. This course assumes you have used a language model through an API, can read a table and a simple cost calculation, and have seen a fitting job described, even if you have never run one; it assumes no deep mathematics and no prior operations experience.
Prefer to learn live and in person? The same material anchors Cognosco's one-day programs — browse the in-person catalog.
Start with Production Is a Different Problem
$195 — introductory price. One payment. No subscription, no password to manage.
Here is exactly what happens when you enroll:
- Pay securely with Stripe — you are taken straight into the course
- Create your learner profile and begin the Module 1 starting-point check
- Save the access key you are given — it restores your enrollment on any device
- Learn at your pace; your progress is stored in your browser on your device
For the full voice experience, use Chrome and allow the microphone when asked. Typing your answers works everywhere, all the time. Questions before you enroll? Email hello@cognoscolearning.com.
Before you enroll
What is a Socratic coaching session?
A conversation, not a video. Your coach asks a question; you answer — out loud by voice, or by typing — and the coach responds to what you said: naming the misconception it hears, affirming what you got right, and asking the next question. You advance only when you have mastered each concept, so a finished course means the learning held.
How long does it take — and how long do I have?
About 30 minutes per concept. Learn in sittings of any length; your progress is saved as you go. You have a full year from purchase to finish, and you can revisit and retake within that year.
What does the certificate verify?
That you completed every concept and both capstones of this course. Anyone can confirm a Cognosco certificate online — enter its certificate ID on our verification page.
What if it isn't for me — what are the refunds?
If you have not started, cancel for a full refund within seven days of purchase. Once you have started, there is no cash refund — stop before finishing and we credit the full amount you paid toward any other course in the library (credit valid one year from your original purchase). Once you complete the course, no refund or credit applies.
More Signature Courses are coming
MLOps & LLM Fine-Tuning in Practice: Take Models from Lab to Production is Signature Course No. 314. Leave your email and we'll send you news of future Signature Courses and Lighthouse updates as they are announced — nothing else.