Service
MLOps & AI Model Lifecycle Management
Building the model is only part of the job. Getting it into production and keeping it reliable over time is what matters.
Audit My ML InfrastructureMLOps & Model Lifecycle
Most AI projects break down after the demo stage. A model may look strong in testing, then struggle in production because performance slips, edge cases pile up, or no retraining workflow exists to maintain it. Katalyst AI Lab builds the infrastructure that keeps models usable in the real world, with version control, monitoring, evaluation, and retraining systems built in.
Services
What We Build
| Service | Description |
|---|---|
| ML Pipeline Design & Build | End-to-end pipelines for data ingestion, preprocessing, training, evaluation, and serving. Reproducible, version-controlled, and built to run across cloud environments. Every run is logged and every artefact is tracked. |
| CI/CD for ML Models | Automated testing and deployment workflows for models, triggered by new data, scheduled retraining, or performance alerts. Models are validated in staging before they move to production. |
| Model Monitoring & Drift Detection | Ongoing tracking of prediction quality, data drift, and confidence calibration using Evidently AI, Arize Phoenix, or custom Prometheus and Grafana setups. |
| A/B Evaluation & Canary Deployment | Controlled production experiments that compare model versions before full rollout. Includes shadow deployments and traffic-split testing to reduce release risk. |
| Model Registry & Versioning | MLflow or W&B-based registries that track experiments, metrics, artefacts, and deployments. Useful for audit trails, debugging, rollback, and compliance reviews. |
| Cloud ML Infrastructure Setup | End-to-end setup on AWS SageMaker, GCP Vertex AI, or Azure ML, including compute provisioning, autoscaling, IAM setup, cost control, and VPC networking. |
Technology
Our Stack
Production-grade MLOps tooling for pipelines, experiment tracking, serving, and monitoring.
Kubeflow Pipelines
Prefect
Apache Airflow
GitHub Actions for ML
MLflow
Weights & Biases (W&B)
Neptune.ai
BentoML
Seldon Core
Ray Serve
Torch Serve
FastAPI + Uvicorn
Evidently AI
Arize Phoenix
Prometheus
Grafana
PagerDuty
Slack
Docker
Kubernetes
Karpenter Autoscaling
Select a category to explore our tooling
FAQ
Questions
What does MLOps mean in practice?
MLOps brings software delivery discipline to machine learning. In practice, that means pipelines that train, test, and deploy models automatically, monitoring that flags performance issues early, version control for data and models, and governance systems that support audits and compliance.
Do we need MLOps from day one?
Not always. A first proof of concept or internal tool may not need a full MLOps setup. But if your model serves customers, supports critical decisions, or needs to improve over time, the right infrastructure matters early. We help define what is necessary now and what can wait.
Can you work within our existing AWS / Azure setup?
Yes. We work inside your existing cloud environment and IAM structure. We also support hybrid and multi-cloud setups, and can operate within data residency, security, and approved-tooling constraints.
Is Your AI Deployment Production-Ready?
We'll assess your current ML infrastructure and tell you exactly what is and isn't production-grade. No commitment required.
Audit My ML Infrastructure