Service
Machine learning models that reach production
Your data science team built a good model in a notebook. Six months later it's still there, and the business keeps deciding on gut feeling. The model works; what's missing is everything around it.
Why models stay in notebooks
A model in a notebook answers a question once. A model in production answers it every day, with data that changes, on infrastructure that has to stay up, for people who need to trust it. That second part is where most projects stall: feature pipelines, deployment, monitoring, retraining, and a clear owner.
We build both parts. The data scientists who design the model sit next to the engineers who deploy it, which means the model is designed from day one to run in production.
Results we can point to
- At Moni a delinquency prediction model improved recovery in the high-risk segment by 20%, and the training cycle got three times shorter.
- At CCM/DataVue we have 30+ response and conversion models in production. Owned models reduced scoring error by 30% compared with the vendor they replaced, and credit offer conversion rose by 130%.
- At TravelX a flight occupancy model replaced manual reference curves, cut the error by more than half and analyzes 400 flights a day. It took 2 months from kickoff to production.
- At CTF Capital we built real-time prediction for crypto markets on 12+ AWS services and made model-to-production implementation eight times faster.
Diagnosis first
Before training anything we look at the data you actually have, the decision the model will support and how its output will be used. If the business process can’t act on a prediction, a better model won’t change anything. That conversation saves more projects than any algorithm.
After deployment
Models degrade. Customer behavior shifts, sources change, a feature stops being populated. We set up monitoring that catches this and a retraining path your team can run. If you’re running several models, that is where MLOps comes in.
What you get
A model tied to a business metric
We define success before training anything: recovery rate, conversion, forecast error. Accuracy on a test set isn't the goal.
A production deployment
Batch or real-time inference running on your infrastructure, integrated with the systems that act on the predictions.
Monitoring and retraining
Drift and performance checks, and a retraining path that doesn't depend on one person remembering to run a notebook.
A team that understands the model
Code in your repositories, documentation and walkthroughs, so your team can maintain and evolve it.
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
Moni
Delinquency prediction and data infrastructure that reduced operational costs by 50% and improved loan recovery by 20%.
Transportation
TravelX
Flight occupancy model that replaced manual heuristics for a company that sells predictive intelligence to airlines.
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.
The uncomfortable truth about AI: what if it's not for your business?
Many companies feel they need to adopt artificial intelligence, but it's not always the right call. In this post we help you figure out if your business is actually ready to benefit from AI, how to avoid misguided investments, and why a solid data strategy is still the real differentiator.
Frequently asked questions
We already have a model in a notebook. Can you take it to production?
Yes. We review it first, because sometimes the notebook hides data leaks or features that won't exist at prediction time. Then we package it, deploy it and add monitoring.
How do we know if machine learning is worth it for our problem?
That's what the diagnosis is for. Sometimes the answer is a rule, a SQL query or a better dashboard, and we'll tell you. When ML does make sense, we estimate the impact before building.
How long until a model is in production?
At TravelX it took 2 months from kickoff to production. Larger engagements, like 30+ scoring models at CCM/DataVue, run in stages so each model starts creating value before the next one is ready.
Who owns the models?
You do. The code, the data and the trained models live in your accounts and repositories.