MLOps Services and ML Pipeline Development
Get models into production and keep them healthy.
MLOps services cover the engineering that gets a machine learning model out of a notebook and into production, then keeps it working. NetOfficials builds ML pipelines for training and deployment, adds monitoring, and sets up retraining so a model does not quietly get worse as your data changes. Building the models themselves is covered by our machine learning development services.
- Building software since
- 2011
- Approved before the build
- Written scope
- Tracked in Jira
- Sprint demos
- Confirmed in the contract
- Your IP
What Is Included in Our MLOps Services
A model in production needs the same care as any other software, plus checks that suit models. These are the pieces we put in place.
Training Pipelines
Repeatable steps that fetch data, train a model and record exactly what was used.
Model Packaging and Serving
Models wrapped as services that your applications can call reliably.
Model Registry and Versioning
A record of every model version, its data and its results, with simple rollback.
Monitoring and Drift Alerts
Dashboards and alerts that flag when inputs or predictions move away from what the model learned.
Retraining Workflows
Scheduled or triggered retraining with checks before a new model replaces the old one.
Testing and Release Gates
Automated checks on data and model quality that must pass before anything goes live.
Who Our MLOps Services Suit
MLOps matters once models have to work for real users. These are the situations we hear about most.
-
Data science teams
Move models out of notebooks
Turn working experiments into services that engineers and applications can depend on.
- Pipelines your team can rerun
- Deployment without manual steps
- Documentation for handover
-
Teams with models already live
Stop silent decay
Find out when predictions drift and have a clear process to fix them.
- Monitoring of inputs and outputs
- Alerts that reach the right person
- Controlled retraining
-
Engineering and platform teams
Standardise how models ship
Give every data scientist the same route to production instead of one-off scripts.
- Shared pipeline templates
- Automated tests and approvals
- Fits your existing CI/CD
Managed ML Platform or a Pipeline You Assemble?
Cloud providers sell managed ML platforms, and open-source tools let you assemble your own. Each suits different teams. We compare them against your cloud, skills and constraints in discovery.
A managed ML platform is usually the better choice when
- Your team is small and wants less to maintain
- You already run most workloads on one cloud
- Your needs match the platform's standard features
- You prefer predictable, supported tooling
| Consideration | Managed ML platform | Self-assembled pipeline |
|---|---|---|
| Setup effort | Quicker to start | More to design and wire up |
| Flexibility | Works within the platform's design | Pick each component |
| Portability across clouds | Tied to the provider | Easier to move |
| Operational load | Provider runs the platform | Your team runs and patches it |
| Cost pattern | Service fees add up with use | Lower fees but more engineering time |
| Best for | Small teams on one cloud | Teams with platform skills and special needs |
How We Deliver Your ML Pipelines
Every project moves from a written scope to agreed review points, so you always know what is happening and what comes next.
Review Your Models
We look at how models are trained and used today, the data they depend on and the pain points, and agree a scope in writing.
Signed scopeDesign the Pipeline
We plan data steps, training, testing, deployment and monitoring, and choose tools with you.
Approved designBuild and Automate
Pipelines, serving and tests are built in sprints, with a demo of the working flow each sprint.
Automated pipelineDeploy and Monitor
Models go live behind release checks, with dashboards and alerts set up from the first day.
Models in productionRetrain and Improve
Retraining rules are agreed and tested, and your team is shown how to run and extend them.
Ongoing upkeep
What Affects the Cost of MLOps
MLOps scope depends on how many models you run and how they are used, so we scope each engagement from the facts. The written scope shows how these drivers apply to you.
Number of models
More models mean more pipelines, tests and monitoring to set up.
Data sources and freshness
Models that depend on many sources, or on fast-changing data, need more robust pipelines.
Serving requirements
Real-time predictions with low latency need more engineering than scheduled batch runs.
Depth of monitoring
Tracking accuracy against real outcomes takes more setup than basic health checks.
Compute and storage
Training jobs, GPUs and stored model versions add running cost.
Governance needs
Approval steps, audit trails and access rules add design work. We confirm them up front.
Why Choose NetOfficials for MLOps
The working terms below are part of every engagement, not extras.
One Cross-Functional Team
Design, development, testing and DevOps run through a single team, so decisions stay connected.
Senior From Day One
The senior engineer who scopes your work stays involved while it is delivered.
You Own Everything
IP assignment is confirmed in the contract, and you receive the full source code and documentation at handover.
Mutual NDA
We sign a mutual NDA before you share the details of your project.
Your Working Hours
We agree a communication window that suits your time zone at the start.
Visible Progress
Work is tracked in Jira, with a demo and your feedback at the end of every sprint.
How We Choose the Right Stack for Your ML Pipelines
We recommend technology for your project and your team's future, not for our convenience. Three questions guide every choice.
Will it handle your load?
We size the design for your expected users, data and growth, so it does not need rebuilding when the business grows.
Will it fit your systems?
We check how the stack connects with the tools, databases and partners you already rely on before we commit to it.
Can you support it later?
We favour well-supported technology with a wide talent pool, so your own team or any future partner can maintain it.
Technologies we work with
TensorFlow
Python- LLM APIs
Docker
Kubernetes
Terraform
AWS
Azure
Google Cloud
PostgreSQL
MongoDBFlexible Ways to Work With Us
Pick the model that fits where your project is today. You can move between them as it grows.
Project Delivery
We deliver an agreed scope, timeline and price.
- Written discovery scope
- Milestone reviews
- Best for a clear first release
- For ongoing work
Dedicated Team
A team that works only on your project, led by a named delivery lead.
- Cross-functional team
- Sprint demos and planning in Jira
- Scale the team up or down
Team Extension
Specialists who join your existing team, tools and processes.
- Works in your process and tools
- Agreed overlap hours
- Best for adding a missing skill
More From Our AI Development Practice
This service is part of our ai development practice. These pages cover the services most often needed alongside it.
MLOps FAQs
What is MLOps?
MLOps is the set of practices and tools for deploying, monitoring and updating machine learning models in production. It brings the discipline of software delivery to models and their data.
What is an ML pipeline?
It is an automated series of steps that prepares data, trains a model, tests it and releases it. Because the steps are scripted, they can be rerun and checked rather than done by hand.
Why do models get worse over time?
The real world changes, so the data a model sees in use drifts away from the data it learned from. Monitoring spots this and retraining brings the model back in line.
Do we need MLOps if we only have one model?
A single model still benefits from reliable deployment and basic monitoring. We scope the setup to the size of your need and avoid adding machinery you will not use.
Can you work with the models our data scientists already built?
Yes. We review the existing code and training process first, then plan how to package and automate it, and agree the plan in a written scope.
Who owns the pipelines and models?
You do. The pipelines, deployment scripts, trained models and documentation made for your project are assigned to you under the contract, and we hand them over with the code.
Ready to Run Your Models in Production?
Share a few details. A senior engineer reads every brief and replies with questions and a suggested next step.
- We review your briefAnd reply with questions
- We sign a mutual NDABefore you share the details
- You get a written scopeTeam, timeline and model, before any commitment