Netofficials delivers MLOps services and ML pipeline development for teams whose models work in notebooks but not reliably in production. One cross-functional team, led by a named delivery lead, builds repeatable training, deployment and monitoring pipelines inside your stack, with a client review every sprint. Send a short brief to start.
We turn your experiment code into automated pipelines for data preparation, training, validation and deployment. Each run is repeatable, so you stop depending on one person's notebook or a manual handover. You get versioned models, tracked experiments and a release path your engineers can read, rerun and extend.
We put your models behind a batch job or a real-time API, connected to the applications and data sources that depend on them. Staging and production environments are set up with the deployment steps documented. That lets your product team ship model updates without rebuilding the integration each time.
Production models degrade as data changes. We set up monitoring for model performance, data drift and pipeline failures, with alerts routed to your team. Retraining triggers are agreed with you in writing, so you decide when a model is refreshed and how a bad release is rolled back.
You walk us through your models, data sources, current training process and where releases break down. We put the pipeline goals and first-release boundaries into a written discovery scope.
You receiveYou agree the written discovery scope
You review the proposed pipeline architecture for training, deployment and monitoring, along with the environments and tooling it will use. Our technical architect explains the system design and technology choices so you can challenge them early.
You receiveYou approve the architecture before development starts
We develop the pipeline components in sprints, covering data and feature steps, automated training, model deployment and monitoring. You see working builds rather than status slides.
You receiveYou review the pipeline at the end of every sprint
We test the pipeline end to end against the acceptance criteria you agreed. That includes retraining runs, deployment steps, rollback and the monitoring alerts your team will depend on.
You receiveYou accept the pipeline against the agreed criteria
Netofficials deploys the training, deployment and monitoring pipelines to your production environment. You then receive the documentation and deployment access your team needs to run and extend them.
You receiveYou confirm the handover of documentation and access
Send a short brief covering your models, data sources and where they run today, and we reply with questions, a proposed scope and the team to deliver it, or a different approach if MLOps is not the right fit.
Your estimate starts with how many models you run and how each one is trained, validated and retrained. We list the data sources feeding them, whether the pipeline is batch or real-time, and which stages need automation. That list sets the size of the work and removes surprises from the written discovery scope.
Cost and timing also depend on where the pipeline has to run and what it connects to. We map your cloud or on-premise environments, data warehouses, feature stores, CI/CD tooling and the applications that call the models. Knowing this early removes rework when deployment access and handover are planned.
Decide who watches models once they are live. We agree what to monitor, such as prediction quality, data drift and pipeline failures, who responds, and what your team needs to run it alone. Documentation and deployment access are handed over at release. If MLOps is not the right fit, we will recommend another approach after scoping.
Fixed-price delivery of a defined MLOps scope, such as a training-to-deployment pipeline with monitoring, for teams that know which models and systems are involved.
An India-based development team that works through your MLOps roadmap over time, suited to companies with several models to take into production.
Individual engineers who join your data science or platform team to add pipeline automation, deployment and monitoring skills inside your existing process.
Netofficials confirms your MLOps tooling in the written discovery scope, so nobody is surprised by it mid-build. The technical architect, who is accountable for technology selection, bases the choice on your existing cloud, data and CI/CD setup, the frameworks your models already use and who will run the pipelines after release. You avoid a rebuild around tools your team does not want to maintain, and you receive documentation and deployment access at handover.
React
Node.js
Python
PostgreSQLCost and timing depend on the scope we agree in writing, not on a fixed rate card. The main drivers are the number of models, the data sources feeding them, the integrations with your existing systems, and the environments the pipeline must run in, such as development, staging and production. Netofficials sets out assumptions, review milestones and commercial terms before delivery begins, and we confirm the estimate after the written discovery scope. Send a short brief and we will reply with questions and a proposed scope.
You own the code. IP assignment is confirmed in the contract before the build begins, so the training, deployment and monitoring pipelines we write for you are yours. A named delivery lead owns day-to-day progress and communication, and you have one point of contact throughout. We agree the communication window, meeting schedule and update cadence with you before work starts, and each sprint ends with a client-facing review. At release we hand over documentation and deployment access.
Yes. We can build around your cloud environment, tools and the models your data scientists already have, with responsibilities and review points agreed together. Choose fixed-price project delivery for a defined pipeline, a dedicated India-based team for an ongoing roadmap, or staff augmentation to add engineers to your team. If MLOps is not the right step for your situation, we will recommend a different approach after scoping.
Tell us which models you have, where they run today and what keeps them from running reliably in production. We reply with questions and a written scope for your training, deployment and monitoring pipeline, and with the engagement model that fits. A named delivery lead handles your communication. IP assignment is confirmed in the contract before the build begins. If MLOps is not the right fit, we will recommend another approach after scoping.
