YakData Forward · Forward-Deployed AI Engineering
First, see what a Forward-Deployed AI Engineer actually does through one real production system. Then use the same methods, artifacts, and review gates on your own work.
Project File 001 follows 77 Rules from consequential business decision through requirements, architecture, vertical slice, evaluation, production hardening, deployment, handoff, and improvement. You see the model, rules, deterministic controls, evidence, human review, failures, and operating decisions together.
Episode 1 publishes Wednesday, August 19, 2026. The free seven-part FDE Lab is the public proof layer of YakData Forward.
See the standard for free. Apply it with the Field Library. Put your own work under scrutiny in FDE Production Reviews.
Watch real production judgment applied to a real system.
Free public proofUse the same working artifacts instead of starting from a blank page.
$495/yearBring your actual work into weekly experienced external scrutiny.
$1,500/quarter · Field Library includedDocument what was reviewed, what changed, and what evidence supports the system.
Reviewed does not mean passed01 · The production standard
Production AI starts with a decision that matters and ends when the system can be tested, run, recovered, and owned.
02 · Visible proof · Project File 001
77 Rules keeps the series attached to one real system: the decisions, architecture, model boundaries, failures, controls, and handoff stay visible. The case page separates what the live product proves from later client adoption and extension.
What the case exposes
A persuasive dashboard may not contain enough evidence to prove the claim. The system sometimes has to ask for context or say “insufficient evidence.”
Public product example: the same Marketing Channel Dashboard looked strong on design while integrity exposed missing comparative context, an unclear time range, and inconsistent monetary units.
03 · From public proof to your work
The free material teaches the production standard in public. Paid access does not hide the method. It adds reusable working assets and experienced judgment applied to your own system.
Follow 77 Rules through the complete Forward-Deployed AI Engineering job, with real artifacts, failures, tradeoffs, role overlap, and production decisions.
Use the working system yourself: requirements, architecture, vertical-slice, evaluation, failure-testing, readiness, deployment, and handoff assets.
Bring real work into weekly live review when a decision benefits from experienced external scrutiny. Field Library access is included while active.
A Production Review Record can show what was examined, what evidence was presented, and what changed. A reviewed stage is not automatically a passed stage.
Choose the path by the job in front of you
YakData Forward does not split these audiences into separate courses or tiers. The same production method applies; the right next action depends on whether you are learning the whole job, building now, developing an engineer, or hiring YakData to own delivery.
Best fit: experienced technical practitioners moving toward end-to-end FDE responsibility.
“Can I reason across the whole production job, not only my current specialty?”
Senior/staff engineers · AI/ML engineers · data engineers · solutions architects
“What am I missing before this becomes a production problem?”
Engineering managers · technical leaders · data/AI leaders · team-development sponsors
“How do I strengthen cross-functional production judgment on work the team actually owns?”
CTO · CIO/CDO · VP Engineering/AI/Data · operating executives
“Can this problem become a production system our team can run and own?”
One offer, not three practitioner tiers. Aspiring FDEs, active builders, and managers use the same $1,500/quarter FDE Production Reviews when review is the right next step. Private organizational delivery remains a separate YakData engagement.
A note from Stephen
I’m Stephen McDaniel. I’ve spent most of my career in the awkward space between a business problem and the system that eventually has to work. FDE Lab is where I take that work apart in public: the architecture, decisions, tests, revisions, and tradeoffs that usually disappear from a polished demo.
Sometimes the right answer is more AI. Sometimes it is a rule, a better workflow, a simpler system, or not building the thing at all. I’ll show you the evidence and tell you what I think.
Stephen McDaniel · Founder, YakData
04 · The 7-episode journey
Each episode opens on a real artifact, shows the decision at stake and what can go wrong, and closes with the next build, test, release, or handoff decision.
Define the decision, owner, scope, definition of done, and build-or-stop choice.
Proof: decision map + scope/acceptancePreview Episode 1 →Turn expertise into rules, evidence, context, workflow states, errors, and testable contracts.
Proof: rule/evidence schemaPreview Episode 2 →Design the architecture, frontend/backend contracts, constraints, observability, and overlapping work.
Proof: role/dependency graphPreview Episode 3 →Move one representative case through every critical layer before broadening the backlog.
Proof: vertical-slice architecturePreview Episode 4 →Benchmark the real task, test critical misses and false alarms, and verify human control.
Proof: benchmark + failure matrixPreview Episode 5 →Harden validation, retries, versions, regression, telemetry, deployment, and recovery.
Proof: production-readiness checklistPreview Episode 6 →Package the runbook, test evidence, recovery knowledge, extensions, metrics, and next decision.
Proof: ownership/handoff packagePreview Episode 7 →05 · Why YakData
FDE Lab comes from systems I have built through YakData. The detailed proof page shows the work, the numbers, and the boundaries around the claims.
For teams building this now
If your team is trying to turn an AI experiment into something people can rely on and own, YakData handles the definition, build, evaluation, deployment, and handoff.
Choose the next action
