MLOPS & MONITORINGMLOps & Monitoring for Models in Real Use
A model is not finished when training stops. We help you keep it running, watch if the answers get worse, and retrain when the data change. If you already have a model in production, we can look at that too.
def check_live(batch, baseline): score = evaluate(batch) if drifted(score, baseline): alert('model needs review') return score150+
Projects delivered
2012
Year we started
80+
Clients
98%
Client satisfaction
About MLOps & Monitoring
MLOps is the habit of treating a model like other software. You save versions, you deploy in a known way, and someone need to watch it after it goes live. Monitoring is just checking if the answers still look right when new data comes in.
Companies use it because a model don't stay accurate forever. Customers change, products change, and the numbers drift. We keep the talk simple. We tell you what we will watch, who gets the alert, and when it is time to train again.
Why Choose MLOps & Monitoring
You know which file is live
Versions are named and stored, so you are not guessing which model the server is using.
Problems show up earlier
If scores drop or the input looks odd, you get an alert before customers complain for weeks.
Retrain is a job, not a panic
The same steps can run again when you have new labels, instead of one person doing it by hand.
Fits the stack you have
Python, PyTorch or TensorFlow models. Docker and AWS if that is where the rest of the app already lives.
We start with what you need
Some teams only need tracking and a weekly check. We do not build a huge platform for no reason.
What We Do With MLOps & Monitoring
We help you to track runs, ship the model and watch it. Usual tools are MLflow, Docker, AWS, PyTorch and TensorFlow. Kubeflow only if the team is already on Kubernetes and it actually helps.
Track experiments
MLflow or a similar log so you can see what data, settings and score belong together.
Ship in a repeatable way
A Docker image and a simple release path, so the next version is not a special laptop copy.
Watch live answers
Basic charts and alerts when the input or the score moves too far from what you expect.
Plan the next train
When the data change, we set a path to retrain, check, and swap the live file.
Benefits for Your Business
Less surprise in production
You see a drop in quality instead of finding it from angry tickets.
Easier for the next person
Notes and versions mean a new teammate can see what is live without a long hunt.
Safer changes
You can go back to the last good model if a new one is not better.
Time back for the team
Repeat deploy steps run the same way each time, so people are not copying folders by hand.
Ready to talk?
If you have a idea for MLOps and monitoring, or a model that is already live, send us a short note. We will look at it and tell you the next step.
Contact us