Service
MLOps your team can run without us
Your models are in production. Retraining is manual, monitoring is a spreadsheet, and when the data scientist who built them leaves, nobody knows how they work.
MLOps is about who can operate the model
A model that only its author can retrain is a liability. MLOps is the set of practices that makes models boring to operate: every version traceable, every deployment repeatable, every degradation visible before the business notices.
We don’t install a platform and leave. We build the pipelines with your team, on your infrastructure, and make sure they can run them.
What that looks like in practice
- At MODO we built an MLOps framework that MODO adopted and operates on its own today. Refactoring code and infrastructure along the way saved thousands of dollars a month.
- At CTF Capital we handled deployment, retraining and monitoring of predictive models over 28 months, and made model-to-production implementation eight times faster.
- At CCM/DataVue 30+ scoring models run in production on an encrypted data platform that operates at terabyte scale. That’s only possible when deployment and monitoring are standardized.
Right-sized
A company with two models doesn’t need the tooling of a company with two hundred. We start with versioning, automated deployment and basic monitoring, then add continuous training and a feature store when the numbers justify them.
Installed capacity
The deliverable isn’t the pipeline, it’s your team knowing how to use it. We document the framework, pair with your engineers on the first models and step back once they run the next one without us.
What you get
Automated deployment
CI/CD for models: every change is versioned, tested and deployed the same way, with a way back if something breaks.
Monitoring that means something
Data drift, model performance and the business metric the model exists for, with alerts that reach the right person.
Scheduled and triggered retraining
Continuous training pipelines that retrain on fresh data and only promote a new model when it beats the current one.
A framework your team adopts
Templates and documentation so the next model follows the same path without us.
How we work
Diagnosis
The real problem isn't always the obvious one at first glance. We see the situation without the bias of being inside the organization.
Right-sized solution
We propose the simplest and most fitting solution for your specific problem. Never a bazooka to kill a mosquito.
Installed capacity
When we're done, your team has full ownership of the solution. If we keep working together, it's because you want to and we can help, not because we've got you trapped in a black box.
Related cases
Fintech
CCM/DataVue
30+ scoring models on an encrypted data platform that operates in the order of terabytes.
Fintech
CTF Capital
Real-time prediction for crypto markets. 28 months, 12 AWS services, zero excuses.
Fintech
MODO
Big data processing, experimentation platform, and GenAI features for the 4+ million users of Argentina's leading digital wallet.
Further reading
Why most AI projects go nowhere
Most AI projects don't fail technically. They fail to ship. Here are four mistakes we've seen over and over, and what the projects that actually work have in common.
How to use custom containers in SageMaker for inference with LightGBM (step-by-step guide)
Complete guide to running batch inference with LightGBM on AWS SageMaker: learn how to deploy massive, reproducible, and scalable processing using custom containers and predictive AI.
Vibe coding with AI: 10 best practices for coding fast (without losing control)
Vibe coding isn't about delegating everything to AI: it's about AI-assisted programming with good judgment. In this guide we share 10 practices for documenting in natural language, generating tests, applying incremental changes, modularizing, choosing tools, and versioning without drama.
Frequently asked questions
When do we need MLOps?
When you have more than one model in production, when retraining depends on a person remembering to do it, or when nobody can tell you how a model is performing today. Before your first model is live, a lighter setup is usually enough.
Which tools do you use?
The ones that fit your stack. On AWS that often means SageMaker and managed services; elsewhere, open-source tools. The framework matters more than the brand of the tools.
Will our team be able to run it?
That's the goal. MODO adopted the MLOps framework we built and operates it on its own today.
Do we need a platform team first?
No. We set up what a small team can operate and grow it as the number of models grows.