Managed Machine Learning

Training a model is generally the easy part of an ML project. Running that same model for two years, against shifting data, in front of a business that depends on its outputs to function, is where most ML projects quietly fall over and die.

We run the full pipeline on your behalf. You get a model behaving in production, and we take responsibility for everything underneath it.

What "the full pipeline" includes

• Data pipeline. Ingestion, cleaning, validation, and feature engineering. This is the unglamorous eighty percent of any ML project.
• Training and evaluation. On whichever framework fits the problem best, whether that's PyTorch, TensorFlow, scikit-learn, or something custom we put together for the use case.
• Deployment. Versioned, rolled out with shadow traffic and canary patterns, and ready to roll back at short notice.
• Monitoring. Drift detection on the inputs, performance tracking on the outputs, and alerting when something starts looking off.
• Retraining. On a planned schedule rather than in response to a panic. Triggered by data drift or accuracy decay, not by a customer complaint that finally made it onto someone's desk.

When a custom framework makes sense

Most problems can be solved with off-the-shelf models or fine-tuned variants of them. Sometimes the shape of your data, your latency budget, or the accuracy ceiling pushes us toward something more custom. We'll tell you which camp you're in on the first call, and we'll only build something custom when the math actually says it's worth doing.

What you don't have to staff

An ML engineer. An MLOps engineer. A data engineer. An on-call rotation for any of them. Get in touch and we'll walk through what running this looks like in practice.