AI · ML deployment & MLOps
Your trained model isn't in production yet — and until it is, it earns nothing
Most ML projects stall at the same gate: a model that scores well in evaluation, sitting in a notebook, waiting for the engineering work that turns it into a system production can actually use. Banao closes that gap — model packaging, inference serving, data pipelines, monitoring, and the retraining triggers that keep accuracy from decaying after launch.
We run this stack on our own deployed models before we bring it to a client, so the MLOps discipline we build for you is already tested on a real 300-person operation.
Banao— we deploy and run the ML models that score our own engineering applicants, with drift monitoring and retraining built in.
What ML model deployment and MLOps covers when Banao does it
Deployment is not a single step. It is the data pipeline, the model registry, the serving layer, the A/B harness, the monitoring, and the retraining loop — we build the full set, not just the part that shows on a demo.
Model packaging and inference serving
We containerise the model, expose it via a REST or gRPC endpoint, and wire it into the application layer — with latency budgets, batching logic, and versioned rollback built in from the start.
Feature store and data pipeline
A feature store that serves training and inference from the same source, so the features the model was trained on are identical to the features it scores on in production.
CI/CD for ML models
A deployment pipeline that validates a new model version — against a held-out eval set and your production distribution — before it touches live traffic, so a bad retrain can't slip through undetected.
Shadow deployment and A/B testing
We shadow a new model alongside the current one and score both before swapping, so the production impact of any change is measured, not guessed.
Drift monitoring and alerting
Statistical monitoring on input features and prediction distributions that fires before accuracy visibly degrades — giving your team time to act rather than explaining a bad week of decisions.
Automated retraining pipelines
Triggered retraining on a schedule or on drift signals, with validation gates that only promote a retrained model if it outperforms the incumbent on the metrics that matter.
Model versioning and experiment registry
A registry that tracks every model version, the dataset it was trained on, and the eval scores it was promoted on — so rollback is a decision you can make with data, not a guess.
Governance, access control, and compliance logging
Audit logging of what model served what prediction, when, and on which inputs — for the regulated industries where that record is not optional.
Why deployment is where most ML projects die
The notebook-to-production gap is not a data science problem. It is an engineering problem: the feature computation that runs in training doesn't match what the serving layer does in real time, the model has no way to know when its training distribution has shifted, and the retrain process depends on a data scientist running a script by hand. Each of those gaps is a silent accuracy leak that nobody notices until a business decision goes wrong.
Banao's approach treats model deployment as a software delivery problem. We build the feature pipeline, the serving endpoint, the monitoring, and the retrain trigger as a single designed system — not as a sequence of one-offs bolted together after the model scores well on a test set.
Training-serving skew, fixed by design
The features used to score a live request come from the same store and transformation logic as the features used to train. Skew between training and serving is one of the most common causes of production accuracy loss — we eliminate it structurally, not by manual checking.
Drift caught before it costs you
We instrument production inputs and predictions with statistical process control so drift surfaces in a monitoring alert, not in a weekly business review after the damage is done.
Retrain that doesn't require a data scientist every time
The retraining pipeline runs on a trigger — time-based, drift-based, or data-volume-based — validates the new model, and promotes it if it passes. The data scientist reviews a dashboard, not a notebook.
What MLOps means in practice for a team that already has a model
Most teams that come to us have a model they trust in offline evaluation. The question they actually need answered is: how do we run this reliably, keep it accurate, and know when it needs attention — without hiring a five-person platform team to maintain it?
We scope the MLOps layer to what the model's risk profile and business impact actually warrant. A recommendation model that shapes homepage content needs different monitoring than a fraud model that blocks transactions. We build to the right level, not to a template.
The MLOps stack we sell is the one we run ourselves
InterviewGod, the system Banao uses to score and rank its own engineering applicants, runs on a deployed ML model with drift monitoring and retraining pipelines we maintain. We do not outsource the judgment on our own hires to a model we stopped maintaining after launch.
Running production MLOps on our own operation — across a ~300-person engineering company — is what keeps our standards honest. We have been on the receiving end of the drift alerts, the failed retrains, and the versioning decisions. We build your MLOps stack to the same bar.
- InterviewGodScores Banao's own engineering applicants on a maintained, monitored deployed model.
- VikaasBanao's own demand-gen pipeline — a deployed ML system with monitoring we operate ourselves.
When full MLOps is more than you need right now
Not every deployed model needs a feature store and a full retraining pipeline on day one. We will tell you when to start simpler:
- Low prediction volume: if the model runs hundreds of times a day rather than millions, a lightweight serving layer and manual retrain cadence may be the right starting point.
- Stable distribution: if the inputs to your model are unlikely to drift — because the real world doesn't change fast for your problem — continuous monitoring adds cost without proportionate value.
- Prototype phase: if you are still validating that the model's predictions change outcomes, spend the engineering budget on evaluation and iteration before you build production infrastructure for it.
How we start — scope the deployment before we build it
Deploying a model badly is worse than not deploying it. We pin the serving requirements, data dependencies, and monitoring needs before we write the pipeline.
- AI Discovery Sprint2 weeks · fixed price
We audit the model's training and serving requirements, map the data pipeline, identify drift risks, and hand back a deployment architecture, monitoring plan, and effort estimate — yours to keep. If you continue, the Sprint is credited against the build.
- Deployment and MLOps build
We build the feature pipeline, serving endpoint, model registry, monitoring, and retraining trigger as a designed system — not a collection of scripts. The eval harness comes with it.
- Operated production
We run the monitoring, handle retrain promotions, and respond to drift alerts — and we hand over the operational playbook so your team can take ownership when they are ready.
Frequently asked questions
What is the difference between ML model deployment and MLOps?
Deployment is the act of making a model available to a live system — packaging it, exposing an endpoint, and wiring it to the application that uses the prediction. MLOps is the operational layer on top: the data pipeline, model registry, CI/CD, monitoring, and retraining that keeps the deployed model accurate over time. Both are needed for a model that stays useful after launch.
How do you handle the feature pipeline for production inference?
We build a feature store that serves training and inference from the same transformation logic, so the features the model was trained on match what it sees in production. Training-serving skew — where the offline and online feature computation diverge — is one of the leading causes of silent accuracy loss, and we eliminate it by design.
What does model drift monitoring actually catch?
We monitor two things: data drift (the distribution of inputs shifting from what the model was trained on) and concept drift (the relationship between inputs and the right output changing). Both cause silent accuracy degradation. The monitoring fires an alert before the degradation shows up in your business metrics, giving your team time to retrain rather than explain.
How do you test a new model version before it goes to production?
New versions pass through a validation gate in the CI/CD pipeline: eval on a held-out set, a shadow deployment period where both models score live requests and we compare outputs, and a metric threshold before the promotion. A retrained model that doesn't outperform the incumbent doesn't get promoted, regardless of how good it looked in offline evaluation.
Can you deploy on our existing cloud infrastructure?
Yes. We deploy to AWS, GCP, Azure, or your own data-centre environment. The serving and monitoring stack is designed around your infrastructure, not a specific cloud vendor. If you have existing data pipelines or a preferred orchestration layer, we build to fit them rather than replacing them.
How long does an ML deployment project take?
A two-week Discovery Sprint maps the architecture and effort. The build — feature pipeline, serving layer, monitoring, and retraining — typically runs 6–10 weeks depending on data complexity and the number of integration points. Banao's bench means work begins in weeks, not months.
What happens to accuracy after you hand over the system?
The monitoring and retraining pipelines continue running. Drift alerts fire when the model needs attention; the retrain pipeline validates and promotes the new version if it passes the gate. We can operate the system for you, hand it over with an operational playbook, or do both in a transition.
How do we get started if we already have a trained model?
Bring the model, the training data description, and the application it needs to serve. The Discovery Sprint audits the deployment requirements, identifies the drift risks, and produces a deployment architecture. If the existing model isn't production-ready for a different reason — evaluation gaps, training data issues — we surface that in the Sprint rather than after the build.
Get your trained model into production
Bring the model you trust in evaluation. In 45 minutes we'll tell you what the deployment architecture should look like, where the drift risks are, and what the MLOps build would take.
Book a 45-min scoping call