It works only in a notebook
The model runs on one person's machine and cannot be deployed.
What we doMove the code into versioned pipelines that anyone on the team can run.
Bridging the technology gap
We take machine learning from a notebook to production and keep it there: pipelines, deployment, monitoring and retraining, with the same discipline as any other software.
Monitor and retrain. Watch accuracy and drift, and retrain when the data changes
MOUNTSMY provides MLOps services from Ahmedabad, India: reproducible training pipelines, model deployment and serving, monitoring for drift and quality, and automated retraining, on AWS, Azure or Google Cloud. It applies the DevOps habits of automation and review to machine learning, in your own accounts.
Most ML value is lost between the experiment and the product.
An example path, drawn by us. Your assessment shows yours.
Most are engineering problems, not modelling problems.
The model runs on one person's machine and cannot be deployed.
Nobody remembers which data or settings produced the good model.
Releasing a model means copying files and hoping.
Real-world data changes and accuracy falls without anyone noticing.
Refreshing a model takes weeks of manual work.
Expensive hardware sits idle or is oversized.
Every model follows the same path, and each step has a check. If a check fails, the release stops.
MLOps is less visible than modelling, and it decides whether the model delivers value.
Models reach users in days, not months.
Any model can be rebuilt from its data and code.
You hear about falling accuracy before users do.
Models refresh on a schedule or trigger, with checks.
GPU and cloud spend right-sized and watched.
Who trained what, on which data, and when.
MLOps borrows from DevOps and adds what is special about data and models.
| Aspect | DevOps | MLOps |
|---|---|---|
| What is released | Code | Code, data and the trained model |
| What can go wrong | Bugs and outages | Bugs, outages and models that quietly get worse |
| Testing | Unit and integration tests | Those, plus data checks and model evaluation |
| After release | Monitor the service | Monitor the service and the model's accuracy and drift |
We use the managed platform of your cloud or open-source tools, whichever fits.
Turn notebooks into repeatable data and training pipelines.
We build alongside your data science team, and nothing is forced on them.
Look at how models are built and released today.
Walk us through your models and give read-only access.
We change nothing in this stage.
Put tracking, versioning and pipelines in place.
Review pull requests and try the pipeline.
Built beside your current process.
Release a first model through the pipeline.
Approve the first release.
The old way stays available until you trust the new one.
Watch quality, drift and cost.
Agree thresholds and contacts.
Alerts are tuned to avoid noise.
Retrain and improve without a project each time.
Keep it yourselves, or keep us on monthly support.
Everything is in your repositories and accounts.
We bring DevOps discipline, and we work with your data scientists, not around them.
Most ML platforms fail on adoption, not technology.
Every release has a way back, and nothing reaches production without a check.
Most clients begin with the assessment.
A short review of how your models are built and released, with a ranked plan. Yours to keep.
Good for: Knowing where to startA clear scope, timeline and price for a defined piece of work.
Good for: A specific goalFixes, audits and short tasks, billed by the hour.
Good for: Small or urgent jobsPart-time or full-time engineers working inside your tools, by the month.
Good for: Ongoing work and supportMonitoring and audit needs differ by sector.
It connects model building with cloud and delivery.
A way of building, releasing and running machine learning with the habits of DevOps: automated pipelines, versioning, testing, deployment and monitoring, plus what is special about data and models.
Because a model that works in a notebook does not survive production by itself. Data changes, models drift and manual releases break. MLOps keeps models working and reproducible.
DevOps releases code. MLOps releases code, data and a trained model, and has to watch for the model's accuracy falling over time.
The slow loss of accuracy that happens when real-world data moves away from the data the model was trained on. Monitoring catches it early.
AWS SageMaker, Azure ML and Google Vertex AI, or open-source tools such as MLflow, Airflow and Kubeflow. We choose what fits your team.
Yes. We build around their workflow and train them on the pipelines.
Yes. We start with an assessment, then move them into pipelines step by step.
Not always. Managed serving or simple containers are often enough to start.
By right-sizing, scheduling and sharing capacity, and setting budgets and alerts.
You do. They live in your repositories and accounts, with documentation.
Book a free 30-minute call with an engineer. We’ll send a written plan and quote after.