AI-first GCC

MLOps & LLMOps.

Model lifecycle management, CI/CD for ML, experiment tracking, monitoring, and drift detection so your AI systems run reliably in production.

Models in notebooks don't create business value. MLOps is what turns AI experiments into reliable, monitored, governed production systems.

Deliverables

What we deliver

Model lifecycle management

Manage the full lifecycle from experimentation through training, validation, deployment, and retirement.

CI/CD for ML

Automate model testing, validation, and deployment with ML-specific continuous integration and delivery pipelines.

Experiment tracking

Track experiments, parameters, metrics, and artifacts for reproducibility and auditability.

Production monitoring

Monitor model performance, latency, throughput, and business metrics in real time.

Drift detection & retraining

Detect data and concept drift automatically and trigger retraining workflows when needed.

Frequently asked questions

What is the difference between MLOps and LLMOps?

MLOps covers traditional ML model lifecycle. LLMOps extends this with prompt management, evaluation frameworks, and cost controls specific to large language models.

Do we need MLOps if we only have a few models?

Yes. Even a small number of production models benefit from automated testing, monitoring, and deployment practices.

What tools do you use for MLOps?

We work with MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, and other platforms depending on your stack.

How long does it take to implement MLOps?

A foundational MLOps setup typically takes 6–12 weeks. Full maturity is iterative and grows with your AI program.

Can you integrate with our existing CI/CD?

Absolutely. We extend your existing DevOps pipelines with ML-specific stages rather than replacing them.