ML PIPELINES
Production ML infrastructure — training pipelines, model serving, monitoring, version control, and continuous evaluation. The unglamorous engineering that turns models into reliable products.
WHAT WE BUILD
Reproducible, versioned training workflows with data validation, experiment tracking, and automated retraining.
Low-latency, autoscaling inference services with batching, caching, and graceful degradation.
Detect data drift, model decay, and quality regressions before they affect users — with alerting.
Track data, models, and code together so every prediction is reproducible and auditable.
Automated eval suites and A/B testing so you know a new model is actually better before it ships.
Centralised, consistent features across training and serving to eliminate train/serve skew.
CAPABILITIES
Trigger retraining on schedule or drift, with validation gates before anything reaches production.
Safely compare models in production with shadow deployments and controlled rollouts.
GPU/CPU autoscaling, batching, and multi-model serving optimised for cost and latency.
Right-sized infrastructure, spot instances, and caching keep inference costs under control.
Robust DAGs with retries, observability, and clear failure handling across the ML lifecycle.
Model registry, approvals, and lineage for compliance and reproducibility.
PROCESS
Define goals, success metrics, data sources, and constraints. A fixed-price sprint delivers a spec and roadmap you can take anywhere.
WK 1–2System design, evaluation strategy, and infrastructure plan — built for production scale and observability from day one.
WK 2–4Build the training, serving, monitoring, and evaluation infrastructure — orchestration, registry, feature store, and CI/CD for models.
WK 4–10Benchmarking, load testing, error handling, monitoring, and cost controls before anything ships to users.
WK 10–12Milestone-gated go-live with monitoring and alerting, then continuous evaluation and improvement as usage grows.
WK 12+TECHNOLOGY STACK
Orchestration
Tracking
Serving
Features
Infra
Monitoring
USE CASES
Serve recommendation, ranking, or prediction models to millions of requests reliably.
Keep models fresh with pipelines that retrain and validate as new data arrives.
Catch silent model decay in fraud, risk, and forecasting systems before it costs you.
Give data science teams a paved road from notebook to production with guardrails.
Consistent, reusable features shared across teams and models.
Auditable, governed ML for finance, healthcare, and other compliance-heavy domains.
WHY CHOOSE US
We've built the unglamorous plumbing that keeps ML reliable in production.
Nothing ships without proving it's better — continuous eval and A/B by default.
Kubernetes, autoscaling, and cost-aware architecture across AWS and GCP.
Versioned data, models, and code so every result can be reproduced and audited.
We use the right tools for your stack rather than forcing a fixed platform.
We keep the pipelines healthy as your models and data evolve.
FAQ
RESOURCES
READY TO START?
Tell us what you're building. We'll scope it and give you a fixed price and timeline.