ML Pipelines
End-to-end pipelines — data ingestion, feature engineering, training, evaluation and deployment.
MLOps
ML pipelines that stay healthy in production
We build the infrastructure that keeps your models performing — pipelines, monitoring, retraining automation and governance on AWS, GCP or Azure.
AWS, GCP, AzureAny cloud
AutomatedRetraining pipelines
Full observabilityDrift & performance
What We Build
MLOps for every stage of ML production
Model Monitoring
Real-time tracking of prediction drift, data drift, latency and business metrics.
Retraining Automation
Trigger-based or scheduled retraining with automated evaluation and safe promotion.
Feature Stores
Centralised feature repository for consistent training and serving across teams.
Model Serving
Low-latency inference endpoints — REST, gRPC, streaming and batch.
ML Governance
Model cards, lineage tracking, audit trails and compliance documentation.
Our Process
From notebook to production ML system
1
Audit
Review your existing models, data pipelines and pain points.
2
Architecture
Pipeline design, tool selection and infrastructure blueprint.
3
Build
Pipelines, monitoring and serving layer built iteratively.
4
Testing
Load testing, failure mode testing and drift simulation.
5
Handover
Documentation, runbooks and team training.
FAQ
Common questions
Which MLOps tools do you use?
MLflow, Kubeflow, Vertex AI, SageMaker, Feast, Evidently, Seldon and custom tooling — selected for your stack.
Do you work with existing models?
Yes — we productionise models your team has already built, or build the infrastructure around third-party models.
How do you handle model drift?
Statistical drift detection (PSI, KS test) with configurable thresholds, alerting and automated retraining triggers.
Can you work with our existing cloud setup?
Yes — we work within your existing AWS, GCP or Azure account and VPC.
MLOps Services — Production-Grade Machine Learning
TECHTARN provides expert MLOps services, productionising machine learning models with CI/CD, monitoring and drift detection for businesses across the USA, Canada, UK, UAE, Singapore, Australia and worldwide. We bridge the gap between AI experiments and reliable production systems.
Most AI projects fail not in the modelling but in deployment. A model that works in a notebook is very different from one that runs reliably in production, serving real users at scale. Our MLOps expertise ensures your machine learning delivers consistent value long after launch.
Our MLOps Services
We build complete ML infrastructure: automated training pipelines, model versioning and registry, CI/CD for models, real-time monitoring and drift detection, and automated retraining triggers. We work with MLflow, Kubeflow, Vertex AI, SageMaker, Feast and Seldon — selecting the right tools for your stack.
We can productionise models your team has already built, or provide the full infrastructure around generative AI, computer vision and AI agents. Our drift detection catches model degradation early, and our infrastructure works within your existing AWS, GCP or Azure cloud environment.
Reliable AI needs solid MLOps. Whether you need to deploy one model or build a complete ML platform, we deliver production systems you can trust. Explore AI development or contact us for a free MLOps consultation.
Ready to productionise your ML?
Tell us about your models and current pain points. We'll design the right MLOps stack.
Get In Touch
Ready to get started?
Tell us about your project. We reply within 4 business hours.
Send Us a Message