MLOps services that keep models working after launch.
Deployment pipelines, monitoring, drift detection and retraining. The work that decides whether a model is still accurate a year after it shipped. Most engagements start with models that already exist and have no owner, which is a more common situation than it should be. Where the need is building model-backed features rather than operating existing ones, managed AI services covers that work.
What is MLOps, and when is it needed?
MLOps is the engineering discipline around machine learning models in production: deployment, versioning, monitoring, drift detection and retraining. It becomes the binding constraint once a model is live, because accuracy measured at training time says nothing about behaviour six months later on data the model has never seen.
What MLOps engineering covers.
A model in production is running, not finished. Input distributions shift, upstream data changes shape, and accuracy decays without anything visibly breaking. That is the failure mode MLOps exists to catch. The first work is usually establishing a baseline so drift can be measured against something.
- Model deployment
Serving infrastructure built for your traffic and latency needs.
- Pipeline automation
Training and deployment pipelines that run without manual steps.
- Monitoring and drift detection
Model performance tracked in production, not just at launch.
- Infrastructure as code
Provisioning and environments defined and versioned, not clicked together.
- Documentation and handover
Deployment and rollback procedures documented as the system evolves.
Working inside your cloud account.
The work divides into three. Getting a model deployed repeatably. Watching it once it is live. And retraining it before accuracy decays enough for someone to notice. Most teams have the first covered and neither of the other two. Assigned specialists work in your cloud account alongside your data scientists, rather than taking models away and returning them, because the people who trained a model usually understand its failure modes best.
One fixed fee for a monthly average.
We calculate the engineer's working hours across a full year, deduct annual leave and public holidays, then divide by twelve. That average becomes the fixed monthly capacity and price in your agreement, so budgeting for an MLOps engineer stays predictable whether a given month runs light or heavy on hours. Models are only as reliable as the pipelines feeding them, so this capacity is often assigned next to data engineering outsourcing under one agreement, and the arithmetic is set out in how the monthly fee is built.
The MLOps engineering roles we assign.
Take one role, or several as one delivery team. Each role below has its own page describing what it delivers under a service agreement.
Building in-house, a local agency, or Azendo.
Each fits a different situation. An in-house role makes sense when the work is permanent and local; a local agency suits a one-off project with a clear end date. Azendo sits between the two — ongoing capacity for work that keeps coming, with the team, the workplace and the administration behind it handled on our side.
| Comparison | Building it in-house | Local agency | Azendo |
|---|---|---|---|
| Time to productive output | Months — recruit, onboard, ramp up | Fast to start, slow to learn your product | 4–6 weeks |
| Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same delivery team, for years |
| Continuity of product knowledge | Lost when the hire leaves | Ends with the project | Held by the assigned team |
| Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously |
| Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle |
| Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle |
Questions about managed MLOps.
Can I choose the seniority level?
Yes. Junior through principal MLOps engineers are available, priced by level. We'll recommend a level based on the scope you share before delivery starts.
What if the stack isn't listed above?
Tell us what you use. The list above is what we see most often, not a limit — we'll confirm fit for your exact setup at scoping.
Can I add a second MLOps engineer later?
Yes, at the next monthly cycle. Capacity moves with your roadmap rather than locking you into the original scope.
Who owns the infrastructure and code the MLOps engineer writes?
You do. Work happens in your repositories, under your license terms, from the first commit.
Often assigned alongside an MLOps engineer.
A specialist rarely works alone on a roadmap. These disciplines cover the ground around the role and can be added to the same service agreement.
MLOps services for models already running in production
A model in production is running, not finished. Input distributions shift, upstream data changes shape, and accuracy decays without anything visibly breaking. That silent decay is what this work exists to catch.
Most engagements start with models that have no owner. The first task is establishing a baseline, because drift cannot be measured against nothing.
Work happens inside your cloud account, alongside your data scientists rather than instead of them. The people who trained a model usually understand its failure modes best, and keeping them involved beats a clean handover.
Where the pipelines feeding a model are the real problem, managed data services is the place to start, and we will say so rather than instrument something built on an unreliable input.
Tell us what mlops engineering capacity your roadmap needs.
Tell us about your project and the capacity you have in mind. A service delivery manager will get back to you.
Loading the contact form… You can also email hello@azendo.co.
We reply within one working day. No obligation, and no newsletter.