AI & Intelligent Solutions · Service 13 of 14

MLOps & ML Model Engineering models that stay accurate long after launch day

O Clock Software, headquartered in Chennai, India, builds the pipelines and practices that keep machine learning and LLM systems reliable after launch: versioning, automated deployment, monitoring, drift alerts, retraining and rollback.

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  • 2009Founded in Chennai
  • 1,000+Projects delivered
  • 12+Countries served
  • 100+Engineers in-house
  • 4.6★Google rating · 59 reviews

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What we build

MLOps & ML Model Engineering: what we build

Pipelines to deploy, monitor, version and retrain models. Here are the six kinds of MLOps services work we are asked for most often.

MLOps maturity

Where are your models on the MLOps ladder?

Most teams have models stuck at Level 0 or 1. We move you up one level at a time, matched to how many models you run and how often they change.

  1. Level 0Manual

    What it looks like: Models are trained in notebooks and deployed by hand; nobody is sure which version is live.

    What we add next: Version control for code, data and models, plus a model registry.

  2. Level 1Pipeline

    What it looks like: Training is scripted and repeatable; deployment is still manual.

    What we add next: Automated deployment with testing and approval.

  3. Level 2CI/CD for ML

    What it looks like: Models are tested and deployed automatically; retraining is manual.

    What we add next: Monitoring for accuracy and data drift, with alerts.

  4. Level 3Continuous

    What it looks like: Drift triggers retraining; new models are validated and promoted automatically with rollback.

    What we add next: Ongoing tuning, audits and capacity planning.

How we deliver

How we deliver MLOps & ML Model Engineering

  1. 01

    Assess

    Current models, workflow, tools and pain points.

  2. 02

    Design the platform

    Target level, tooling and responsibilities.

  3. 03

    Build pipelines and registry

    Reproducible training and versioned models.

  4. 04

    Add monitoring

    Accuracy, drift and service health with alerts.

  5. 05

    Hand over or operate

    Runbooks and training, or we run it for you.

Technology

Technology for MLOps & ML Model Engineering

Chosen per project for fit, maintainability and your team's skills.

Platforms

  • MLflow
  • Kubeflow
  • Amazon SageMaker
  • Google Vertex AI
  • Azure Machine Learning

Pipelines

  • Airflow
  • Prefect
  • DVC
  • GitHub Actions

Monitoring

  • Evidently
  • Prometheus
  • Grafana
  • Langfuse

Serving

  • Docker
  • Kubernetes
  • KServe
  • BentoML
  • vLLM

Straight answers

Is MLOps & ML Model Engineering right for you?

We would rather recommend less than sell you more. Here is how we judge it.

A good fit when

  • One or more models are in production or close to it
  • Models are updated by hand today
  • Nobody can see live model accuracy
  • Audit or regulation requires traceability

  • One model updated once a year

    A lightweight checklist and scheduled review may be enough; a full platform would be overkill.

  • The model is not yet proven

    Prove the model first; heavy MLOps before that slows learning.

  • The platform is chosen before the problem

    We fit tooling to your team and models, not the other way round.

Use cases by industry

MLOps & ML Model Engineering by industry

The same capability, shaped by the data, rules and users of each sector.

Related services

Often combined with MLOps & ML Model Engineering

Prefer engineers in your own team?

The full service area

FAQ

MLOps & ML Model Engineering FAQ

What is the difference between MLOps and DevOps?

DevOps automates building, testing and releasing application code; MLOps extends this to machine learning, where data and models change as well as code. MLOps adds data versioning, model registries, training pipelines and monitoring for model accuracy and drift.

What is model drift?

Model drift is the gradual drop in a model's accuracy as real-world data or behaviour moves away from what the model learned. MLOps monitoring detects drift early so the model can be retrained or adjusted before decisions suffer.

Do LLM applications need MLOps?

Yes, in a form often called LLMOps: versioning prompts, running evaluation suites on every change, tracking usage and quality, and routing between models. The principles are the same even when you do not train the model yourself.

Which MLOps tools do you use?

We use MLflow, Kubeflow, Airflow and DVC, cloud platforms such as SageMaker, Vertex AI and Azure Machine Learning, and monitoring tools such as Evidently and Grafana, choosing what fits your cloud and team rather than a fixed stack.

Can you set up MLOps for models our team already built?

Yes. We start with an assessment of your current models and workflow, then add versioning, pipelines and monitoring around them, without rewriting models that already work.

Do you provide ongoing model monitoring?

Yes. We can operate monitoring and retraining for you with agreed response times, or hand over runbooks and train your team to run it.

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