Model Operations (MLOps)
AI in production, not proof of concept.
Building an AI model in a notebook is straightforward — operationalising it reliably in production, monitoring for drift, managing versions and automating retraining is what separates a pilot from production AI. Migradia's MLOps engineers build the training, evaluation, deployment and monitoring pipelines that keep your AI models accurate, auditable and compliant after launch. We deploy model registries, automated retraining triggers and bias-monitoring pipelines so your ML team ships models with confidence.
- ML training & evaluation pipeline automation
- Model registry & versioning (MLflow / Weights & Biases)
- Production deployment (SageMaker / Vertex AI / Azure ML)
- Drift detection & automated alerting
- Automated retraining pipeline design
- Bias, fairness & explainability monitoring
We assess your data estate — quality, governance, lineage and architecture — before designing any analytics or AI capability.
Lakehouse or warehouse architecture designed for your workload, compliance requirements and team capability.
Ingestion, transformation, modelling and serving layers built, tested and documented by our data engineering team.
Training, documentation and a structured handover programme. Your team operates the platform independently after engagement.
Models promoted to production within 2 weeks of approval
Drift detected and retraining triggered automatically
ML team deployment frequency 5× baseline
Get a scoped 30-minute consultation. We map your scenario and propose a clear delivery plan — no commitment required.
Book a consultation