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A model that works in a notebook is not a system your business can depend on. We build the MLOps foundation — CI/CD pipelines, scalable serving, drift monitoring and governance — that turns models, LLMs and RAG systems into production AI that stays accurate, reproducible and auditable long after launch day.
Trusted by startups, scale-ups & enterprises worldwide
Without MLOps, AI models often fail in production due to a lack of automation, monitoring and scalable deployment pipelines. Click a panel to see the challenge, how we fix it, and what changes.
The world moved, the data shifted, and accuracy quietly decayed. With no monitoring, you find out from a customer complaint or a bad quarter — long after the model started getting it wrong.
Deployment is a hand-cranked ritual — copy files, tweak configs, hope nothing breaks. Every release is risky, slow and impossible to reproduce, so improvements pile up unshipped.
Data, code and models drift apart with no version links. You cannot reproduce last month's result, cannot debug a regression, and cannot prove to an auditor how a decision was made.
A model that worked in a notebook chokes under real traffic — latency spikes, GPU costs balloon, and there is no autoscaling, no serving layer and no cost control.
Notebooks thrown over the wall, no shared tooling, no reproducible environment. Handover is a rewrite, and the gap between "it works on my laptop" and "it runs in production" swallows months.
We help enterprises close the gap between data science and reliable operations — through automated pipelines, scalable infrastructure, real-time observability, model governance and continuous optimisation.
Before investing, find out how production-ready your ML actually is. A structured review of your pipelines, infrastructure, monitoring and practices — with a prioritised plan to close the gaps.
Replace hand-cranked steps with automated, reproducible pipelines — from data ingestion and training to validation and deployment — so models ship reliably and repeatably.
Get models into production the right way — containerised, versioned, autoscaling and monitored — with the serving pattern that fits your latency and cost needs.
Know how every model is doing in production — accuracy, drift, latency and cost — with alerting that catches problems before your customers do.
Operationalise LLMs and agents specifically — tracing, evaluation, prompt versioning, token-cost control and guardrail monitoring — the parts classic MLOps was never built for.
Stop rebuilding the same features for every model. A feature store gives training and serving one consistent source, killing the train-serve skew that quietly wrecks accuracy.
Design and build the platform your ML runs on — compute, storage, orchestration and networking — sized for real workloads on AWS, GCP or Azure, cloud or hybrid.
Make your ML auditable and compliant — model registries, approval workflows, lineage and access control — so risk and regulators can see exactly what runs and why.
More than a decade delivering measurable results for enterprises, SMEs and technology companies worldwide.
Three teams whose models finally became dependable production systems.
Challenge: Every model update was a manual, error-prone process taking weeks. Improvements queued up unshipped and one bad release had already caused an outage.
Solution: CI/CD pipelines with automated testing, a model registry, canary releases and one-click rollback — deployment turned into a routine, reversible operation.
Challenge: A credit model was silently drifting. Without monitoring, the first signal would have been a compliance finding.
Solution: Drift and performance monitoring with automated alerts and retraining triggers, plus full lineage so every decision was reproducible and auditable.
Challenge: A recommendation model buckled during peak sales — latency spiked and GPU costs ran away with no autoscaling in place.
Solution: Containerised, autoscaling serving with latency targets, GPU optimisation and cost controls, load-tested against peak-season traffic.
Get a free 30-minute MLOps assessment. We'll review your pipelines, serving and monitoring, and tell you the three things worth fixing first — no pitch.
MLOps is only worth doing if it changes something measurable — release speed, reliability, cost or risk. These are the outcomes our clients track.
Automated pipelines turn model deployment from a risky event into a routine, reversible operation.
Monitoring and retraining keep accuracy from decaying silently between launch and the next crisis.
Registries, lineage and approval workflows mean risk and regulators can see what runs and why.
Accuracy, drift, latency and cost on one dashboard, so problems surface before customers find them.
Data science and engineering share one platform, so handover stops swallowing months.
GPU optimisation, autoscaling and right-sizing stop production spend running away with itself.
Reusable pipelines and infrastructure mean model two through ten plug in rather than start over.
Testing, canary rollout and one-click rollback take the fear out of shipping a model.
A structured path from assessing your current setup to a production ML platform your team can run — and keep running as it scales.
Review pipelines, infrastructure, monitoring and practice to establish real production readiness.
Design the MLOps platform, tooling and workflow around your stack, team and workloads.
Build CI/CD, training and data pipelines so models ship reliably and repeatably.
Stand up scalable, versioned, autoscaling serving with canary and rollback built in.
Wire in drift, quality, latency and cost monitoring with alerting and retraining triggers.
Add registries, lineage, approval workflows and access control for audit and compliance.
Hand over documented tooling and train your team to run and extend the platform.
Tune cost, latency and reliability continuously, and scale the platform to the next model.
Book a free, no-obligation MLOps assessment. We'll review where you stand, tell you honestly what's missing, and give you a costed plan to make your models reliable in production.
Depth across pipelines, serving, monitoring, infrastructure and governance — the engineering that keeps production ML accurate, affordable and auditable.
Automated training, testing and deployment pipelines that make shipping a model routine and reproducible.
Drift, quality, latency and cost monitoring with alerting and automated retraining triggers.
Consistent training-and-serving features that kill train-serve skew and speed up the next model.
Real-time and batch serving — containerised, autoscaling and GPU-aware — tuned for latency and cost.
Tracing, evaluation, prompt versioning and token-cost control for LLM and agent systems.
ML platforms on AWS, GCP and Azure — compute, storage and orchestration, cloud or hybrid.
Reproducible, version-controlled infrastructure with Terraform and Kubernetes, not click-ops.
Model registries, lineage, approval workflows and audit trails for regulated environments.
Connecting pipelines, stores, serving and monitoring into one coherent, maintainable platform.
A vendor-agnostic MLOps stack across clouds and tools — chosen to fit your infrastructure, team and budget rather than a platform we resell.
MLOps helps enterprises across regulated and data-intensive sectors deploy, monitor, govern and continuously improve AI in production.
MLOps keeps AI running. Explore the services that build what we help you operate.
Readiness assessment, use-case prioritisation and a costed roadmap.
Bespoke ML, vision and decision systems trained on your data.
Production-grade LLM, RAG and agent systems.
Connect AI to your CRM, ERP and existing workflows.
Pipelines, warehousing and BI to power your AI.
Automate the workflows RPA cannot handle.
The reason teams trust us to keep their models running.
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They delivered beyond expectations — our project saw real growth thanks to ZTS Infotech's passion and commitment
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They went above and beyond to support us during challenging times. The ZTS Infotech team delivered with dedication, professionalism, and care — truly a partner we can count on!
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Outstanding collaboration and flawless execution — ZTS Infotech made the entire process smooth and truly result-driven!
A partner that treats production ML as an engineering problem with owners — so your models stay accurate, affordable and auditable.
We build ML systems to run, not to demo. Everything is designed for the day it meets real traffic, real data and real auditors.
MLOps, LLMOps, data engineering and the models themselves under one roof — we operationalise what we also build.
We design around your cloud, your stack and your budget — not around a platform we happen to resell.
CI/CD, infrastructure-as-code, testing and version control — 15+ years of shipping software, applied to ML.
Governed, auditable ML for regulated sectors — finance, healthcare and beyond — where compliance is not optional.
Access control, lineage, audit trails and data-retention policy designed in from the first sprint.
Tell us what you've built and where it's struggling in production. We'll come back with an honest assessment, the fixes that matter most, and a transparent estimate — free.
MLOps is the discipline of running machine learning in production reliably — the pipelines, monitoring, versioning, serving and governance that keep models accurate, reproducible and auditable after launch. Without it, models degrade silently, deployments are slow and risky, results cannot be reproduced, and nobody can prove to an auditor how a decision was made. It is the difference between a model that demos and one your business can depend on.
MLOps borrows CI/CD, automation and infrastructure-as-code from DevOps, but adds the things unique to ML: models degrade as data drifts (code does not), results must be reproducible from specific data and code versions, and you have to monitor accuracy, not just uptime. It also has to bridge data scientists and engineers, who often work in very different tools.
That is the most common reason clients come to us. We assess your current setup, then build the CI/CD pipelines, serving infrastructure and monitoring that turn deployment into a routine operation and give you visibility into how every model is performing. Usually we can do this without disrupting the models themselves.
Both. LLMs and agents need their own operational layer — LLMOps — covering tracing, evaluation, prompt versioning, token-cost control and guardrail monitoring, which classic MLOps was never designed for. We build and run both, and increasingly the two together.
With monitoring that watches input data and predictions for drift and performance decay, alerting that fires when quality slips, and automated retraining triggers so the model refreshes before the business feels it. You get dashboards showing accuracy, drift, latency and cost rather than finding out from a complaint.
We are vendor-agnostic. We work across AWS SageMaker, Azure ML, Vertex AI and Databricks, with tools like MLflow, Kubeflow, Airflow, Weights & Biases, LangSmith and the rest, on Kubernetes. We recommend the stack that fits your cloud, team and budget rather than one we resell.
Yes. We add model registries, end-to-end lineage linking data, code and models, approval workflows and access control, so you can reproduce any result and show a regulator exactly what ran and why. This is essential in finance, healthcare and other regulated sectors and is built in from the start.
In most cases, yes. We design the MLOps platform around your current cloud and stack rather than replacing it, adding the pipelines, serving and monitoring layers where they are missing and integrating with what already works.
Yes. We hand over documented, infrastructure-as-code platforms and train your team to operate and extend them. You can run it yourself, or keep us on as a dedicated MLOps team — there is no lock-in either way.
A maturity assessment and roadmap is a small fixed-price engagement. Building the platform is priced fixed-scope or as a monthly dedicated team, depending on how much you want built versus advised. The initial assessment call is free.
