MLOps Consulting

Your model works in staging.
Ours work in production.

Most ML failures happen after training. We build the infrastructure, monitoring, and pipelines that keep your models healthy and reliable - at any scale.

What MLOps covers

Model serving

Low-latency, scalable inference endpoints that handle production traffic.

Monitoring & observability

Real-time visibility into model health, latency, throughput, and error rates.

Data validation

Schema enforcement and statistical checks on every inference and training run.

CI/CD for ML

Automated testing, validation, and deployment pipelines for model updates.

Retraining pipelines

Triggered or scheduled retraining that keeps models fresh as data shifts.

Resource optimisation

Right-sizing inference infrastructure and batching to improve serving efficiency.

73%

of ML projects never make it to production

Not because the model was bad - because the infrastructure wasn't ready. No monitoring, no drift detection, no retraining strategy, no reliable serving layer. MLOps closes that gap.

Model monitoring framework

Data drift

Statistical tests on input distributions - catch distribution shift before it degrades predictions.

Concept drift

Monitor the relationship between inputs and outputs over time, not just the inputs alone.

Performance degradation

Ground-truth comparisons, proxy metrics, and business KPI correlation where labels are delayed.

Infrastructure metrics

GPU utilisation, memory pressure, queue depth, and p99 latency tracked alongside model metrics.

Implementation phases

Phase 1

Audit

Map current serving, monitoring, and deployment gaps.

Phase 2

Instrument

Add logging, metrics, and alerting to every model endpoint.

Phase 3

Automate

Build CI/CD and retraining pipelines that run without human intervention.

Phase 4

Optimise

Improve serving efficiency and reliability through continuous tuning.

The MLOps difference

Without MLOps

Models deployed manually, monitored never.

✕Deployment is a Slack message and a prayer

✕Model performance drifts silently for months

✕Retraining requires a data scientist's full week

✕No visibility into GPU usage per prediction

✕A failed model means hours of incident response

With MLOps

Automated, observable, continuously improving.

✓Zero-touch deployment with automated validation gates

✓Drift alerts fire before business KPIs degrade

✓Retraining pipelines run on schedule or trigger

✓Usage dashboards per model, per endpoint, per day

✓Rollback in one command with full audit trail

Common questions

MLOps FAQ

Do we need MLOps if we only have one model?

Yes - especially if that model is customer-facing. A single unmonitored model can degrade silently and affect revenue before anyone notices. The overhead of basic monitoring and a retraining pipeline is far lower than a production incident.

How is MLOps different from DevOps?

DevOps handles code. MLOps handles code plus data plus models - all of which can change independently and all of which can silently break the system. Model monitoring, data validation, and experiment tracking have no direct DevOps equivalents.

Can you integrate with our existing stack?

Yes. We work with Airflow, Prefect, Kubeflow, SageMaker, Vertex AI, Azure ML, and custom pipelines. We assess your existing infrastructure first and build on it - not around it.

How long does an MLOps engagement take?

A baseline implementation - logging, monitoring, and a retraining pipeline for an existing model - typically takes 4–6 weeks. Greenfield infrastructure for a new ML platform is 8–12 weeks depending on complexity.

Who owns the infrastructure after you leave?

You do. We document everything, train your team, and write runbooks. For clients who want ongoing support, we offer a monitoring and maintenance retainer.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

MLOps Consulting

Your model works in staging.
Ours work in production.

Most ML failures happen after training. We build the infrastructure, monitoring, and pipelines that keep your models healthy and reliable - at any scale.

What MLOps covers

Model serving

Low-latency, scalable inference endpoints that handle production traffic.

Monitoring & observability

Real-time visibility into model health, latency, throughput, and error rates.

Data validation

Schema enforcement and statistical checks on every inference and training run.

CI/CD for ML

Automated testing, validation, and deployment pipelines for model updates.

Retraining pipelines

Triggered or scheduled retraining that keeps models fresh as data shifts.

Resource optimisation

Right-sizing inference infrastructure and batching to improve serving efficiency.

73%

of ML projects never make it to production

Not because the model was bad - because the infrastructure wasn't ready. No monitoring, no drift detection, no retraining strategy, no reliable serving layer. MLOps closes that gap.

Model monitoring framework

Data drift

Statistical tests on input distributions - catch distribution shift before it degrades predictions.

Concept drift

Monitor the relationship between inputs and outputs over time, not just the inputs alone.

Performance degradation

Ground-truth comparisons, proxy metrics, and business KPI correlation where labels are delayed.

Infrastructure metrics

GPU utilisation, memory pressure, queue depth, and p99 latency tracked alongside model metrics.

Implementation phases

Phase 1

Audit

Map current serving, monitoring, and deployment gaps.

Phase 2

Instrument

Add logging, metrics, and alerting to every model endpoint.

Phase 3

Automate

Build CI/CD and retraining pipelines that run without human intervention.

Phase 4

Optimise

Improve serving efficiency and reliability through continuous tuning.

The MLOps difference

Without MLOps

Models deployed manually, monitored never.

✕Deployment is a Slack message and a prayer

✕Model performance drifts silently for months

✕Retraining requires a data scientist's full week

✕No visibility into GPU usage per prediction

✕A failed model means hours of incident response

With MLOps

Automated, observable, continuously improving.

✓Zero-touch deployment with automated validation gates

✓Drift alerts fire before business KPIs degrade

✓Retraining pipelines run on schedule or trigger

✓Usage dashboards per model, per endpoint, per day

✓Rollback in one command with full audit trail

Common questions

MLOps FAQ

Do we need MLOps if we only have one model?

How is MLOps different from DevOps?

Can you integrate with our existing stack?

Yes. We work with Airflow, Prefect, Kubeflow, SageMaker, Vertex AI, Azure ML, and custom pipelines. We assess your existing infrastructure first and build on it - not around it.

How long does an MLOps engagement take?

Who owns the infrastructure after you leave?

You do. We document everything, train your team, and write runbooks. For clients who want ongoing support, we offer a monitoring and maintenance retainer.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

MLOps Consulting

Your model works in staging.
Ours work in production.

Most ML failures happen after training. We build the infrastructure, monitoring, and pipelines that keep your models healthy and reliable - at any scale.

What MLOps covers

Model serving

Low-latency, scalable inference endpoints that handle production traffic.

Monitoring & observability

Real-time visibility into model health, latency, throughput, and error rates.

Data validation

Schema enforcement and statistical checks on every inference and training run.

CI/CD for ML

Automated testing, validation, and deployment pipelines for model updates.

Retraining pipelines

Triggered or scheduled retraining that keeps models fresh as data shifts.

Resource optimisation

Right-sizing inference infrastructure and batching to improve serving efficiency.

73%

of ML projects never make it to production

Not because the model was bad - because the infrastructure wasn't ready. No monitoring, no drift detection, no retraining strategy, no reliable serving layer. MLOps closes that gap.

Our MLOps stack

Serving

TorchServeBentoMLTriton

Monitoring

EvidentlyPrometheusGrafana

Pipelines

AirflowPrefectKubeflow

Registry

MLflowW&B

Model monitoring framework

Data drift

Statistical tests on input distributions - catch distribution shift before it degrades predictions.

Concept drift

Monitor the relationship between inputs and outputs over time, not just the inputs alone.

Performance degradation

Ground-truth comparisons, proxy metrics, and business KPI correlation where labels are delayed.

Infrastructure metrics

GPU utilisation, memory pressure, queue depth, and p99 latency tracked alongside model metrics.

Implementation phases

Phase 1

Audit

Map current serving, monitoring, and deployment gaps.

Phase 2

Instrument

Add logging, metrics, and alerting to every model endpoint.

Phase 3

Automate

Build CI/CD and retraining pipelines that run without human intervention.

Phase 4

Optimise

Improve serving efficiency and reliability through continuous tuning.

Result

"Reduced model failure incidents by 91% with drift monitoring + auto-retraining"

MLOps in numbers

91%

Reduction in model incidents with drift monitoring

3×

Faster deployment cycles with automated ML CI/CD

40%

Average serving efficiency improvement through right-sizing

< 24h

Mean time to auto-retrain on detected data drift

The MLOps difference

Without MLOps

Models deployed manually, monitored never.

✕Deployment is a Slack message and a prayer

✕Model performance drifts silently for months

✕Retraining requires a data scientist's full week

✕No visibility into GPU usage per prediction

✕A failed model means hours of incident response

With MLOps

Automated, observable, continuously improving.

✓Zero-touch deployment with automated validation gates

✓Drift alerts fire before business KPIs degrade

✓Retraining pipelines run on schedule or trigger

✓Usage dashboards per model, per endpoint, per day

✓Rollback in one command with full audit trail

Common questions

MLOps FAQ

Do we need MLOps if we only have one model?

How is MLOps different from DevOps?

Can you integrate with our existing stack?

Yes. We work with Airflow, Prefect, Kubeflow, SageMaker, Vertex AI, Azure ML, and custom pipelines. We assess your existing infrastructure first and build on it - not around it.

How long does an MLOps engagement take?

Who owns the infrastructure after you leave?

You do. We document everything, train your team, and write runbooks. For clients who want ongoing support, we offer a monitoring and maintenance retainer.

Client result

"We went from manually retraining every 3 months to a system that retrains automatically - and our prediction accuracy improved 18% in the first quarter."

Head of ML Engineering - FinTech SaaS, Series B

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

Agentic AI development and multi-agent systems
Generative AI consulting and LLM integration
RAG development and custom model deployment
Data engineering, MLOps and custom software

[email protected]

Your model works in staging.Ours work in production.

What MLOps covers

Our MLOps stack

Model monitoring framework

Implementation phases

Models deployed manually, monitored never.

Automated, observable, continuously improving.

MLOps FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Your model works in staging.Ours work in production.

What MLOps covers

Our MLOps stack

Model monitoring framework

Implementation phases

Models deployed manually, monitored never.

Automated, observable, continuously improving.

MLOps FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Your model works in staging.Ours work in production.

What MLOps covers

Our MLOps stack

Model monitoring framework

Implementation phases

Models deployed manually, monitored never.

Automated, observable, continuously improving.

MLOps FAQ

Ready to build yournext digital product?

Tell us aboutyour project

Your model works in staging.
Ours work in production.

Ready to build your
next digital product?

Tell us about
your project

Your model works in staging.
Ours work in production.

Ready to build your
next digital product?

Tell us about
your project

Your model works in staging.
Ours work in production.

Ready to build your
next digital product?

Tell us about
your project