Login AI
Building MLOps platforms
We build an MLOps platform: reproducible training pipelines, a model registry, quality monitoring, and automated rollout with rollback.
Training, versions, and model rollout are described as code and reproduce with one command.
Pricing
from ₽3,000,000
per project
This is the lower bound of a project. We scope the exact quote after a brief: it depends on volume and the number of integrations.
Capabilities
Reproducible pipelines
Training runs from code and configuration, not from a notebook on an engineer's machine.
Model registry
Every version carries metrics, dataset, and author: it is clear what was trained on what.
Quality monitoring
The platform watches drift and production metrics and warns before degradation.
Rollout with rollback
A new version ships canary-style, and rollback is one command, not a manual file swap.
From request to result
1
Process audit
We review how models are trained, versioned, and shipped today: where the manual steps are.
2
Pipeline schema
We fix the training pipeline, registry, and rollout rules, and agree on quality metrics.
3
Platform and registry
We build the reproducible pipeline and registry and move the first models onto the platform.
4
Operations and monitoring
We enable drift monitoring and canary rollout, and define rollback and on-call.
Check if it fits you
Good fit
More than one model in production
With several models, manual rollout becomes a bottleneck and a source of errors.
Regular retraining
Models update on a schedule or on drift: the platform automates the cycle.
Not a fit
One model, updated once a year
With one rare update, a platform costs more than manual rollout and does not pay off.
No model owner
Without an engineer who owns the platform, it goes stale faster than the models.
What you get
1
command to roll back instead of a manual swap
Models were trained and shipped through the platform, and a version rollback took minutes.
The same result
Reproducible training
Code, data, and parameters are fixed, so training is reproducible and the result repeats a month later.
An answer for prod
A version registry
The registry keeps metrics and dataset per version, so the question "what is in production" has an answer. Model versions reach review.
Before metrics fall
Early drift
Monitoring catches drift before metrics fall for users. Degradation shows early, while it is still cheap to fix.
One-command rollback
A safe rollout
A canary release and a one-command rollback replace manual artifact swaps. The rollout stays safe at any moment.
How the system is built
A reproducible pipeline
The pipeline captures code, data, and parameters in one place, so training starts with a single command and repeats with the same result on any teammate's machine.
A model registry
We keep every model version, its metrics, and the dataset in the registry, so the question of what is in production is answered in a minute.
Monitoring and alerts
Monitoring compares production metrics against the baseline and sends an alert as quality starts to slip, so the team fixes degradation before users feel it.
A safe rollout
The rollout of a new version goes canary-style: first to part of the traffic, then to every user, and rollback runs as one command when metrics drop.
Where the solution pushes back
- 01
A platform is a product
A platform has to be developed and maintained, or it goes stale faster than the models. A platform as a product needs an owner and regular releases.
- 02
Payback grows with model count
Infrastructure for its own sake costs more than manual rollout, so a platform pays back as the number of models grows. Payback depends on the model count and the release cadence.
- 03
An entry threshold
The team needs an engineer who owns the platform and its reliability. The entry threshold grows: without a dedicated engineer the platform goes stale.
- 04
Migrating old pipelines
Moving old pipelines into the registry takes time. Migrating old pipelines is a separate job, and it is planned before the platform launch.
Technologies
The technologies we use in our work and their role in a project.
Pipelines and registry
Pipelines are built in Airflow, model versions are stored in MLflow, artifacts in MinIO, configuration in Kubernetes, and metrics in Prometheus.
Cost calculator
This is an indicative range, not a quote. We scope the exact estimate after a brief: it depends on volume and the number of integrations.
Pipelines
Platform complexity
Multi-cloud
Add-ons
Support
Year one: updates and monitoring, a reply within 2 business days.
Service
Building MLOps platforms
The estimate covers our team's work. Hardware, third-party service licences and travel are added on request.
Estimated cost
₽3.72M
Timeline
8 wk
Frequently asked questions
Do we need Kubernetes?
Not always. At low load the platform is built more simply: reproducibility and the registry matter, not a specific orchestrator.
What about existing models?
We move them onto the platform gradually: new pipelines first, then migrate old models into the registry.
How soon is the effect visible?
The first reproducible pipeline and registry are already a result: training stops depending on an engineer's machine.
Who maintains the platform?
The customer's team with our handover: documentation, training, and support at the start.
What does the platform start with?
With one reproducible pipeline: code, data, and parameters fixed so the result repeats. Then we add a model registry, drift monitoring, and safe rollout.
Can the platform be built on cloud services?
Yes, we build on managed services when they cover the tasks without locking you to one vendor. We weigh your own infrastructure against the cloud by cost of ownership.
How do we measure platform readiness?
By reproducibility: training and rollout run the same on any machine, the model version and dataset are found in a minute, and rollback is one command.
What about legacy pipelines?
We move old pipelines gradually: first we record what already works in the registry, then rewrite to the common standard and add tests. The migration runs alongside the team's current tasks and does not stall the product.
What teams add to this service
Ready to discuss your task?
Leave a request – we'll get back with a proposal and a preliminary estimate.
Contact us