Fractional Teammates · AI & Automation
A senior ML engineer joins a set number of days in your week, inside your repositories and your cloud tenancy. Feature pipelines, deployment, drift monitoring and a retraining schedule that runs without anybody panicking.
A senior specialist, not a junior placement You own the work and the accounts Reply within one business day
Forecast engine
Demand forecast - next 8 weeks
94.2%
Accuracy
+18%
Vs baseline
8w
Horizon
Retrained nightly on 14 months of order behaviour.
OlDevs embeds a fractional machine learning engineer from its Vancouver studio for a set number of days each week.The work is mostly the engineering around the model rather than the model itself: feature pipelines that run on a schedule, reproducible training, evaluation against a plain baseline, deployment as an API or batch job, monitoring for data and prediction drift, and retraining with a rollback path. They work in your repositories, your cloud tenancy and your standups, and document as they go. This is for a workload that keeps arriving after the first model ships, before there is a full week of it.
Key facts
AI & Automation
What this teammate takes off your plate
Feature logic moves out of notebooks into scheduled pipelines with versioned inputs, tested transformations and a backfill path, so the data used for training and the data used at serving time are built the same way.
Reproducible training runs, tracked experiments, and evaluation against a held-out set and a plain baseline, with error analysis on the segments that matter to your organisation rather than one headline metric.
Models served as a batch job, a queue consumer or an API, packaged in containers and shipped through your existing CI and infrastructure, with latency and load behaviour measured before anything reaches users.
Dashboards and alerts for input drift, prediction distribution, data freshness and quality, so a model that degrades quietly is caught by a signal your team owns rather than by a customer complaint.
A retraining programme with defined triggers, an evaluation gate against the live model, versioned artefacts in a registry and a rollback your own team can run, so a refresh is routine rather than an event.
Where a promising notebook has stopped short, we analyse leakage, sampling and evaluation, rebuild the parts that will not survive production, and say plainly when a model is not worth shipping at all.
Benefits
The person choosing the approach has already carried models through several production lifecycles and seen how they fail once real traffic reaches them, a depth that a single organisation's modelling workload rarely keeps occupied full-time.
The gap between a promising notebook and a service your product can call finally has an owner. Your analysts get results into use instead of queueing behind engineering capacity that keeps being reallocated to something else.
Leakage, a split that flatters the result, a metric that hides the class you care about: the ways a model scores well and behaves badly get caught while it is still a candidate, not after your product depends on it.
Predictions get compared with what actually happened, in your own reporting and standups. Your teams learn the difference between a model that is still answering and a model that is still right, rather than after a customer points it out.
How heavy a model is to run, and how long it takes to answer, are known before launch rather than discovered afterwards. Product and engineering can size compute and design the interface around the wait they actually have.
Training environments, the registry and the retraining pipeline stay in your accounts, and the reasoning behind each choice is written where your team will find it. The model can be rebuilt by your own engineers after we leave.
How it works
Scoped days, not vague availability
We agree how many days a week and what those days cover, and quote before the first one. The schedule is fixed enough to plan sprints around, and reviewed openly as the workload changes.
Read the data before promising a model
The first days go into what has actually been collected, how it was labelled, and where the leakage is. Plenty of stalled model work turns out to be a data problem instead.
In your tools and your rituals
Your repositories, cloud account, warehouse, ticket board and standups. Nothing important lives on a studio laptop, and your access controls stay the single place to revoke it.
Weekly demo, written decisions
Each week you see running work rather than a status colour. Model versions, evaluation results and open risks are written down where your own team can read them.
The studio stands behind the person
When a job needs a data engineer, a back-end developer or a security review, the rest of OlDevs steps in. When your engineer is on holiday, the studio still answers.
Who it's for
In each of these the modelling is not the bottleneck. The engineering that keeps a model running is, and it arrives long before there is a full-time job in it.
The prototype works on someone's laptop and has never run on a schedule. What is missing is production engineering around it, not another round of modelling experiments.
One model is serving predictions and nobody clearly owns its monitoring, retraining or failure modes. The work is continuous, and it arrives before anyone has written a job description for it.
Your data team can build a credible model. Pipelines, deployment, versioning and drift monitoring sit outside their day job, and that gap is what keeps work from shipping.
Government bodies, associations and regulated firms that have to show which data trained a model, who approved it and what it refuses to decide. Documentation is part of the build.
12+
Years of studio experience since 2014
1–2
Typical commitment — 1–2 days a week
EN/FR
Languages this engagement can be delivered in
Other fractional roles
The warehouse, the pipelines, the definitions everybody argues about, and a board pack built from the same modelled data as the dashboards.
Explore02Your teammate spends the first weeks counting how often each task is repeated, then automates what is worth automating: triage, routing, document handling, agent workflows.
Explore03A senior DevOps and cloud engineer from our Vancouver studio joins your team for a defined part of each week.
ExploreFAQ
Often not, and it is better to find out in week one. We look at what was collected, how consistently it was labelled, whether the history goes back far enough and where a field leaks the answer. If the records cannot support a dependable model, we say so and scope the data work instead of training something that will look fine in evaluation and fail in production.
No, and treat anyone who does with caution. Accuracy depends on your data and can only be established by testing. What we commit to is method: an honest baseline, a held-out evaluation, error analysis by segment, and a plain statement of what the model can and cannot support. If the results do not clear the bar you set, we say so and recommend stopping.
Hiring gives you full-time attention and the deepest context, and it is the right answer once the work genuinely fills a week. The difficulty is interviewing: a senior ML engineer is hard to assess if nobody in the building has done the job, and a bad hire in this seat costs you months. A fractional teammate gives you ongoing seniority while the workload proves itself. We will not pretend a fractional seat is automatically the cheaper option; that depends on your situation.
When the workload genuinely needs a full week. When a prediction service must be answered around the clock — we do not staff overnight rotas, and a shared week cannot pretend to. When the model is core intellectual property you want built entirely in-house. And when the data is not there: no engineer can produce a dependable model from records that were never collected.
Yours. That usually means AWS, Azure or Google Cloud, Python with the standard training and serving libraries, your orchestrator, your warehouse and your CI. Where no stack exists yet, we choose the smallest one your team can maintain and write down why. We do not introduce a proprietary platform of our own, and we do not ask you to move data to us.
Rarely the model. It is usually a feature pipeline that failed quietly overnight, a source system that changed a column, or a training set that no longer looks like today's traffic. That is why the first weeks go into scheduling, alerting and drift monitoring rather than accuracy: a model nobody is watching gets worse without anybody being told.
Let’s connect
Tell us what is not getting done and roughly how much of a week it needs. We’ll reply within one business day with who would cover it and a tailored quote — no obligation.