Logiciel Solutions Contact Us
Success Stories Tech News Investors Contact Us
whitepaper
6 min read

MLOps and Model Serving Under Regulation.

A supervisory authority asks about one system. On a shared platform the answer comes from the same five or six pieces of code every time: the training job that produced the artefact, the registry entry that describes it, the gateway that logged the request.

That asymmetry cuts both ways. Emit the right records once and thirty teams are evidenced together; emit nothing and thirty teams fail the same audit on the same Tuesday. The duties are written about systems. They are discharged by the platform.

In depth

One Regulator Asks About One System. Five Pieces Of Code Answer.

01

Every team logs, and no two teams log the same thing.

Ask a platform group whether its models are logged and the answer is yes; ask what a record contains and you get as many schemas as there are teams, one with a prompt and a latency, one with a trace carrying no artefact identifier, one writing to a bucket with a thirty day lifecycle nobody chose. Article 12 requires automatic recording across a system's lifetime, which means the record has to be produced by the system rather than by a person who remembered. A logging contract belongs in the serving gateway, where every tenant passes through it whether or not their team wrote the code.

In shortA logging contract belongs in the serving gateway, w…
02

The registry entry cannot reconstruct the run that produced it.

Most registries we open hold a name, a version, a stage tag and a link to a weights file, which answers none of the Article 11 questions: which data snapshot, which preprocessing commit, which environment image, which evaluation result, which person approved the promotion. The fix is a publish step rather than a template, so a training job cannot complete without a dataset snapshot identifier, a code commit, an image digest and an evaluation artefact. Documentation written by a human after the fact is a by-product of nothing.

In shortDocumentation written by a human after the fact is a…
03

Nobody can say which artefact actually answered at 14:05.

Multi-model routing, shadow traffic, canary splits and automatic fallback to a smaller variant are standard practice in any serving tier worth running, and all of them mean the model named in the registry is frequently not the model that produced an output. Where the routing decision is not recorded beside the request, the trace says what should have happened rather than what did. The artefact identifier and the reason for any fallback have to be written at request time by the component that made the choice.

In shortThe artefact identifier and the reason for any fallb…
The detail

Three Emissions, And Where Each One Belongs In The Pipeline.

An obligation is satisfiable only where something in the pipeline produces the artefact that would demonstrate it. These three are the emissions most often missing, and each has a single right place to live.

Zone · 01

The logging contract

Article 12 asks for automatic recording over a system's lifetime. One schema covering request identifier, artefact version, tenant, timestamp, routing decision and outcome, with a retention window chosen deliberately rather than inherited from a bucket rule. Put it in the gateway and no team can opt out, and no team has to implement it twice.

Zone · 02

The registry publish gate

Article 11 technical documentation is assembled from lineage most registries never hold. Make the training job write the entry and fail the run when the dataset snapshot, code commit, image digest, evaluation artefact or named approver is absent. Documentation then falls out of training for every team on the platform at once, rather than being written from memory two years later.

Zone · 03

Upstream checkpoint pins

A modification can reopen the conformity question, and on a platform the change often comes from no release at all. A provider moves a checkpoint beneath a hosted endpoint, a base model is superseded, a runtime quantises differently after an upgrade. Pin the upstream version, record the pin in the registry, and treat a movement as a change event that reaches every tenant it touches.

By the numbers

The figures that make it a board-level conversation.

90%
of organisations report using an internal developer platform, and 76% have a dedicated platform team
2 Dec 2027
Annex III stand-alone high-risk obligations apply, with Annex I following on 2 August 2028
7
duties that reduce to an emission from a training pipeline, a registry publish step or a serving gateway
Inside the report

What you'll take away.

01

Step 1 - Write one logging contract and put it in the gateway

Request identifier, artefact version, tenant, timestamp, routing decision and outcome, with a retention period chosen deliberately and applied to every served model rather than left to whatever each bucket does.

02

Step 2 - Make the training job write the registry entry

Dataset snapshot, code commit, image digest, hyperparameters, evaluation artefact and approver, with the publish step failing when a field is missing. Hand-written entries decay and cannot be audited.

03

Step 3 - Record the routing decision beside the request

Canary splits, shadow traffic and automatic fallback mean the registry entry is not the answer to what served. Write the artefact that actually ran, and the reason it was chosen, at request time.

04

Step 4 - Pin every upstream checkpoint and alarm on movement

A provider-side change reaches every tenant at once. The pin, the recorded version and a change event that notifies each affected system are what stand between you and a silent modification.

Questions

Frequently asked.

Our models run on a managed platform from a cloud provider. Does that discharge these duties?

No. A provider hosts weights and runs infrastructure. It does not write your registry entries, choose your retention window or record which artefact answered a given request. Those emissions are yours, and on a shared platform they are the platform team's rather than each product team's.

Does the AI Act create obligations specifically for ML platforms?

No, and nobody should sell you one. The duties are written about systems and their providers and deployers. What this paper argues is operational rather than legal: on a platform serving thirty teams, the code that satisfies those system-level duties is the same code every time, so it belongs in one place.

The high-risk deadline is December 2027. Can the platform work wait?

Measure 2 December 2027 against how long it takes to change a logging schema across thirty teams and retrain everything that depends on it. Article 50 transparency applied on 2 August 2026 and binds now, and Annex I obligations follow on 2 August 2028. The logging and registry work is the long pole.

What are the penalties if we get this wrong?

Article 99 sets the ceiling for most operator obligations at the higher of EUR 15M or 3% of worldwide annual turnover, with SMEs, start-ups and small mid-caps paying the lower of the two.

The more likely cost is simpler: one missing emission puts every system on the platform in the same position at once.

We already have a control catalogue for production AI systems. Where does this fit?

That catalogue scores one system. This paper maps the legal duties onto the shared pipeline underneath thirty of them. MLOps and Model Serving: An Engineering Reference numbers the twenty-eight platform controls across seven families and names the artefact that proves each one.

Use this one to decide what must exist, and that one to decide who builds it.

Who should read this?

VPs of platform engineering and heads of risk who own the shared path every model takes to production. It assumes you already run a platform, since 90% of organisations do, and that your question is no longer whether to have one but what it has to emit.

Get the whitepaper

Have it emailed to you.

Drop your details and we'll send MLOps and Model Serving Under Regulation straight to your inbox - no spam, unsubscribe anytime.

Download whitepaper
Next step

Instrument the platform once and thirty teams are evidenced together.

Bring your serving path and your model registry to a working session with our platform engineers. We will find out whether either can name the artefact that answered a request last March. SECTION 7 - FAQ - 5 to 8 questions

Book a platform review