Data teams moving models into production
Replace notebook handoffs and manual deployment with reproducible pipelines, artefacts, environments, and release controls.
Model lifecycle, data, evaluation, deployment, observability, governance, and continuous improvement
MLOps services
Rokad establishes reproducible, observable, governed operating systems for machine-learning and generative-AI models across development and production.
Designed for / 01
A focused delivery model for the organisations that need it.
MLOps connects experimentation with dependable operation. Rokad builds data and training pipelines, experiment tracking, registries, evaluation, deployment, model routing, monitoring, feedback, retraining, approval, rollback, and governance for predictive and generative AI systems.
Product teams operating several AI capabilities
Standardise model access, versions, evaluation, observability, cost, rollout, and incident response.
Organisations requiring AI governance evidence
Track data, models, evaluations, approvals, deployments, performance, changes, and ownership across the lifecycle.
Challenges / 02
The problems this service is built to solve.
Experiments cannot be reproduced
Data, code, features, parameters, environments, artefacts, and evaluation results are not consistently versioned or linked.
Production quality is invisible
Teams monitor infrastructure but not data drift, model performance, grounding, failures, costs, or user outcomes.
Model changes lack release discipline
New versions, prompts, providers, indexes, or features reach users without regression evidence, approval, canaries, or rollback.
Capabilities / 03
What Rokad can deliver.
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Data, feature, training, evaluation, and artefact pipelines
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Experiment tracking, lineage, registries, model cards, and reproducibility
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Batch, online, streaming, edge, and generative-AI deployment
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Evaluation suites, regression gates, approval, canary, shadow, and rollback
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Data quality, drift, performance, latency, cost, error, and safety monitoring
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Feedback, labelling, retraining, reindexing, and continuous improvement
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Access, environment, audit, documentation, incident, and governance operations
Solution components / 04
The system behind the visible product.
Development system
Versioned data, code, configuration, features, prompts, environments, experiments, artefacts, and evaluation results.
Release system
Registries, approvals, tests, packaging, deployment, traffic allocation, compatibility, rollback, and change records.
Production intelligence
Data quality, predictions, generations, retrieval, tools, latency, cost, failures, drift, outcomes, and system health.
Improvement loop
Feedback, sampled review, labelling, error analysis, retraining, prompt or index change, evaluation, and controlled release.
Use cases / 05
Where this capability creates practical leverage.
Predictive-model platform
Standardise feature, training, registry, deployment, monitoring, and retraining across several models and teams.
Generative-AI operations
Manage prompts, models, retrieval, evaluations, traces, providers, costs, feedback, and release gates.
Regulated or high-assurance AI lifecycle
Create lineage, documentation, review, approval, access, evidence, monitoring, and change-management controls.
AI platform consolidation
Replace duplicated provider integrations and team-specific tooling with shared services and governance.
Architecture and integration / 06
Designed to fit the wider technology environment.
Artefact lineage
Link production behaviour to data, code, features, prompts, indexes, configuration, model, environment, evaluation, and approval.
Separation of concerns
Keep experimentation, orchestration, serving, evaluation, telemetry, governance, and product integration independently evolvable.
Evidence-based release
Require task-specific regression results, operational checks, approval, staged exposure, monitoring, and rollback before full rollout.
Quality and control / 07
Production requirements are part of the build.
Measured behaviour
Representative evaluation data, quality criteria, failure modes, and release thresholds are defined before expanding production use.
Controlled actions
Permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths govern consequential AI behaviour.
Observable operation
Inputs, outputs, retrieval, tool calls, latency, cost, model versions, and quality trends are monitored appropriately.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the business outcome, users, workflows, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, data, integrations, security, operating model, delivery sequence, and technical decisions.
Build and validate
Deliver in controlled increments with stakeholder review, automated testing, documentation, and production-quality engineering.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence and user feedback.
Typical deliverables
MLOps current-state, requirement, and risk assessment
Data, training, evaluation, registry, deployment, and monitoring architecture
Automated pipelines, environments, artefact storage, and release workflows
Evaluation suites, quality gates, canary, shadow, and rollback controls
Production dashboards, alerts, drift, cost, and performance monitoring
Runbooks, model cards, governance, incident, and handover documentation
Engagement models / 09
Use the delivery structure that matches the work.
Fixed-scope delivery
A defined outcome, scope, acceptance criteria, milestones, and commercial structure for a bounded project.
Dedicated product team
A stable cross-functional team delivering an evolving roadmap with shared product and engineering ownership.
Embedded specialists
Specialist engineers working inside an existing product, technology, data, design, or operations team.
Managed evolution
Ongoing reliability, security, maintenance, feature delivery, and roadmap execution after launch.
Related capabilities / 10
Continue through the wider product and technology system.
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Machine learning
Develop predictive models and decision systems on the MLOps foundation.
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Generative AI
Operate generation, retrieval, prompt, and provider changes with evidence.
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Computer vision
Manage visual datasets, training, edge or cloud deployments, and drift.
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Managed technology services
Ongoing maintenance, cloud, security, reliability, support, and continuous engineering.
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Software development
Custom platforms, backends, integrations, operational systems, and software modernisation.
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Technology consulting and research
Architecture, feasibility, strategy, due diligence, vendor evaluation, and execution planning.
FAQ
MLOps services
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Is MLOps only for custom-trained models?
Is MLOps only for custom-trained models?
No. Generative-AI systems also need versioned prompts, model and provider changes, retrieval indexes, evaluation, traces, cost monitoring, feedback, staged release, and rollback.
02Can Rokad work with our current cloud and tooling?
Can Rokad work with our current cloud and tooling?
Yes. We assess existing data, CI/CD, orchestration, registry, serving, monitoring, and governance capabilities before introducing additional platforms.
03What should trigger model retraining?
What should trigger model retraining?
Retraining may follow new labelled data, drift, performance decline, changed processes, new segments, scheduled cadence, or product requirements, but each version still requires evaluation and controlled release.
04How do you roll back a model?
How do you roll back a model?
We preserve versioned artefacts, configuration, schemas, dependencies, traffic controls, compatibility, and deployment history so a previous approved version or deterministic fallback can be restored.
AI development
Turn models and AI workflows into an operable production capability.
Rokad can establish the pipelines, evaluation, releases, monitoring, ownership, and governance required for continuous operation.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.