Talk to an architect

AI

Models run like production services.

Training pipelines, model registries, GPU scheduling, inference serving and monitoring, so models are deployed, measured and replaced on purpose.

Talk to an architect What is included

Sound familiar

Where this usually starts.

A model in a notebook is an experiment; a model in production is a service with uptime, latency, cost and quality to manage. MLOps is the engineering that makes the difference: reproducible training, versioned models and data, serving infrastructure sized to the traffic, GPU capacity shared fairly between teams, and monitoring that notices when a model's answers drift before your customers do.

Tick the ones that sound like your situation.

Scope

What is included.

  • Training pipelines

    Reproducible training and fine-tuning with tracked data, code and parameters.

  • Model registry

    Versioned models with lineage, approvals and promotion between environments.

  • GPU scheduling

    Kubernetes or Slurm scheduling that shares accelerators between teams by priority and quota.

  • Inference serving

    Low latency serving with batching, autoscaling and quantization where it holds quality.

  • Monitoring

    Latency, throughput, cost and quality metrics, with drift detection.

  • Cost control

    Utilization reporting and right-sizing for training and inference capacity.

Reference architecture

AI that answers from your data, on infrastructure you control.

MLOps runs the loop that keeps models useful: feedback and outcomes flow back into fine-tuning, new versions pass through the model registry, and evaluation decides what reaches production. This service builds and operates that loop.

AI that answers from your data, on infrastructure you control. YOUR DATA DATA AND MODELS SERVING PEOPLE AND OVERSIGHT Documents and wikis Business systems Feedback andoutcomes Ingestion Vector index Fine-tuning Model registry Agent runtime Private model GPU cluster Guardrails Users Evaluation Governance and audit

What this service delivers here

  • Feedback and outcomes

    What users accepted, corrected or rejected: the signal for the next model.

  • Fine-tuning

    Adapts a model to your language and tasks when retrieval alone is not enough.

  • Model registry

    Every model version, its evaluation results and who approved it.

  • Evaluation

    A fixed test set every model change must pass before it ships.

Walk through every flow in AI

Before you start

Decisions to settle early.

  • Kubernetes or Slurm

    Kubernetes suits mixed services and inference; Slurm suits batch training on shared clusters. Some estates run both with a clear boundary.

  • Cloud, owned or both

    Owned accelerators are cheaper at steady use; cloud suits bursts and experiments. The platform is designed so work can move between them.

  • How much to automate retraining

    Automatic retraining keeps models fresh but can ship a bad model quickly. Promotion gates decide what still needs a person.

Approach

How the work runs.

  1. Assess

    Current workflow, capacity and costs

  2. Platform

    Pipelines, registry and scheduling

  3. Serve

    Inference sized to real traffic

  4. Operate

    Monitoring, drift detection and retraining

Commercials

How it is bought.

  • A fixed-fee assessment

    We review how models are trained, deployed and monitored today, and measure accelerator use and inference cost. You receive the gaps, a platform design and a fixed price for building it.

  • A fixed-price build

    Training and deployment pipelines, the registry and promotion workflow, accelerator scheduling, serving and monitoring for the models in scope, at a fixed price.

  • Running it afterwards

    The platform can be handed to your engineers with runbooks, or operated by us monthly, covering capacity, upgrades, failed pipelines and drift alerts.

Start with the assessment.

Scope an assessment

Worked example

Models nobody could reproduce.

Say a retailer's data science team trains demand forecasting models on workstations and a shared GPU server. Nobody can rebuild last quarter's model, and the GPU server is either idle or fought over.

  1. Assess

    We review how models are trained, deployed and monitored today, and measure how the GPU capacity is actually used across the week.

  2. Platform

    Training moves to pipelines that record the data, code and parameters of every run, and models are registered with their metrics and approvals.

  3. Schedule

    The GPUs are pooled under a scheduler with priorities and quotas for each team, so urgent retraining is not queued behind an experiment.

  4. Monitor

    Production models report accuracy against actual sales, and an alert fires when forecasts drift beyond the tolerance the team set.

At the end, any model in production can be rebuilt from its record, GPU time is shared by rule rather than by negotiation, and drift is caught by monitoring rather than by the buyers.

An illustration

An illustration, not a client account.

Where it goes wrong

What we design out.

  • Models nobody can rebuild

    A model trained on one laptop cannot be audited or rolled back. Every run records its data, code and parameters automatically.

  • Accelerators used by negotiation

    Shared GPUs without a scheduler sit idle or oversubscribed. Quotas and priorities decide who runs, not whoever asks loudest.

  • Drift noticed by customers

    A model degrades quietly as the world changes. Production quality is measured against outcomes, and drift raises an alert to the model's owner.

Outcomes

What it changes.

  • Reproducible models

    Any model in production can be traced to the data, code and parameters that produced it.

  • Accelerators kept busy

    Scheduling and quotas raise the utilization of expensive GPUs without starving any team.

  • Quality you can see

    Drift and degradation are alerted on, so retraining is a planned response rather than a crisis.

How you will know it is working

  • Any production model can be rebuilt from its recorded run.
  • Accelerator time is shared by agreed priority, with use reported.
  • A degrading model is flagged by monitoring before users complain.
  • Inference cost is tracked per model and per request type.

You keep

What you have at the end.

Handover pack

five items

  1. MLOps platform design and infrastructure code The architecture of the training and serving platform, and the code that builds it, in your repository.
  2. Training and deployment pipelines Automated pipelines that rebuild a model from its data, code and parameters and promote it between environments.
  3. Model registry with a promotion workflow Every model version with its lineage, metrics and approvals, and a defined path from experiment to production.
  4. Serving endpoints with load test results Inference endpoints configured for your traffic, with latency and throughput measured under load.
  5. Monitoring dashboards and alert routing Latency, cost, quality and drift metrics for each model, with alerts routed to the team that owns it.

The team

Who you work with.

  • ML platform engineer

    Builds the pipelines, registry, scheduling and serving, all as code.

  • MLOps engineer

    Wires monitoring, drift detection and promotion workflows into the platform.

  • Infrastructure engineer

    Sizes and operates the accelerator capacity, on premises or in the cloud.

What we need from you

  • An inventory of the models in use and who owns each.
  • Access to the training data, code and current infrastructure.
  • Data scientists willing to move one model through the new platform first.
  • Agreed quality measures for the models in production.

From the GPU to the governance, one team answers for the model.

Questions

Questions we get asked.

Should we use Kubernetes or Slurm for GPU workloads?

Slurm suits large, long running training jobs that need whole nodes and gang scheduling; Kubernetes suits mixed workloads and inference. Many clusters run both, and we design the split from your mix of jobs.

Can you reduce our inference costs?

Usually. Batching, caching, quantization, smaller models for simpler requests and autoscaling to real traffic each cut cost, and we measure quality at each step so the savings do not come out of the answers.

Do you support on-premises and cloud GPUs together?

Yes. A common pattern is steady workloads on owned capacity and bursts in the cloud, with one scheduler and one registry across both.

How do you detect model drift?

By monitoring the inputs a model receives and the quality of its outputs against a baseline, with alerts when either moves beyond an agreed threshold and a retraining pipeline ready to run.

What is a model registry?

A system of record for trained models: each version with the data and code that produced it, its evaluation results, who approved it and where it is deployed. It is what makes a production model reproducible and a rollback possible.

Can MLOps work with the tools our data scientists already use?

Generally, yes. Notebooks remain the place for exploration, and the platform takes over when work becomes repeatable. MLflow, Kubeflow and Ray integrate with the common frameworks, so for data scientists the change is mostly in how runs are tracked and promoted, not in how models are written.

How do you serve large language models efficiently?

With serving engines built for them, such as vLLM or Triton, which batch requests and manage GPU memory well, and with quantization where tests show it holds quality. Capacity is sized from measured traffic, and autoscaling keeps idle accelerators to a minimum.

Who is on call when a model in production fails?

The model's owner is accountable for its quality, and platform engineers for the infrastructure it runs on. We define both roles and their alerts during the engagement, and where we operate the platform for you, our engineers take the infrastructure side of on-call.

Ask about your situation

Answered from this site, with its sources.

Where it is bought

MLOps and model operations, by sector and region.

Sectors that buy AI

Region homes, and who signs

Often paired with

What usually comes next.

Everything in AI

CONTACT

Turn models into services you can operate.

Share how models are trained and served today. We will design the platform that makes them reproducible and measurable.

  • Senior-led
  • Accountable
  • Direct
  • Fixed-price options
  • Compliance-ready
  • 24/7 capable

Prefer to talk it through? Call +92 (333) 32 11011

What happens next

  1. You pick the time

    Tell us which models you run and on what infrastructure; we schedule the call around you.

  2. Thirty minutes, with an engineer

    An ML platform engineer discusses how training, deployment and monitoring work today.

  3. A written assessment

    You receive a written view of the gaps, a platform design and a fixed price, kept by you either way.