AI & Cloud Integration
ML models that get to production — and stay reliable once they're there.
The gap between a working ML model in a Jupyter notebook and a reliable ML feature in production is wider than most teams expect. The model needs to be served at low latency at scale, retrained on fresh data without downtime, monitored for performance drift, and versioned so regressions can be rolled back. Most ML teams build this infrastructure ad hoc — a collection of scripts and cron jobs that only the person who wrote them understands. We build MLOps infrastructure that treats the ML pipeline as production software: version-controlled, tested, monitored, and operable by the whole team. Whether you're serving scikit-learn models or fine-tuned LLMs, the infrastructure principles are the same.
What's included
- Model training pipeline (automated, reproducible)
- Model serving with low-latency inference
- Feature store design & implementation
- Model versioning & registry
- Performance monitoring & drift detection
- A/B testing & shadow deployment infrastructure
How we deliver
- 1MLOps maturity assessment
- 2Training pipeline build (automated & reproducible)
- 3Model serving infrastructure (batch & real-time)
- 4Feature store & data pipeline
- 5Model registry & versioning setup
- 6Monitoring dashboard with drift alerts
10×
faster model retraining with automated pipelines vs manual
100%
training runs logged and reproducible
<100ms
P95 inference latency on optimised model serving
Automated
drift detection alerting on every production model
Technologies we use
- MLflow
- Kubeflow
- AWS SageMaker
- Azure ML
- Vertex AI
- Ray Serve
- BentoML
- Apache Airflow
- Feast
- Tecton
- Prometheus
- Evidently AI
Why Origin for MLOps & AI Infrastructure
Automated retraining pipelines, not scheduled manual runs
Training pipelines triggered by schedule or performance thresholds, logged and reproducible. Manual retraining is a production reliability risk.
Drift detection monitoring as standard
Every model we deploy to production has data and concept drift monitoring. Model degradation is caught by alerts — not by user complaints.
Model optimisation for production latency
We profile and optimise models for inference — ONNX, quantization, batching strategies — before benchmarking serving infrastructure. The model and the infrastructure are co-optimised.
Industries we serve
Fintech
Fraud detection models, credit scoring, transaction classification
E-Commerce
Recommendation engines, demand forecasting, dynamic pricing
Healthcare
Diagnostic models, patient risk scoring, clinical prediction
Manufacturing
Predictive maintenance, quality control, supply chain forecasting
Logistics
Route optimisation, anomaly detection, delivery time prediction
Media & Adtech
Content recommendation, ad targeting models, audience prediction
“Our data scientists were spending 30% of their time on infrastructure and deployment. Origin built us an MLOps platform on SageMaker — now models go from training to production in hours, not weeks, and every run is logged. The team focuses on the actual ML work.”
Frequently asked questions
- What does MLOps actually include — is it just Kubernetes for ML?
- MLOps covers the full lifecycle: data versioning and validation, automated training pipelines, experiment tracking, model versioning and registry, model serving (real-time API and batch), monitoring for data drift and model performance degradation, and A/B testing infrastructure to compare model versions safely in production. Kubernetes is part of the infrastructure layer but MLOps is more than container orchestration — it's the discipline of operating ML models as reliably as any other production software.
- Our data scientists retrain models manually — what's the problem with that?
- Manual retraining is slow, non-reproducible, and doesn't scale. When a model's performance degrades and needs to be retrained urgently, manual processes require the person who knows the training script to be available immediately. When a regulatory audit requires proof of exactly which data and hyperparameters produced the model in production, manual processes can't provide it. Automated training pipelines solve both: retraining is triggered by a schedule or a performance threshold, every run is logged, and any historical model version can be reproduced exactly.
- How do you serve ML models at low latency and high throughput?
- The right infrastructure depends on your latency requirements and traffic profile. For real-time inference at <100ms: model optimization (ONNX export, quantization, TensorRT for GPU models), a dedicated serving framework (Ray Serve, BentoML, Triton Inference Server), horizontal scaling behind a load balancer, and a model cache for frequently requested inputs. For batch inference at high volume: Spark or Ray for distributed processing with checkpointing. We profile your model and traffic pattern before recommending — the right architecture differs significantly between a recommendation engine and a fraud detection model.
- How do you detect when a model's performance is degrading?
- Model monitoring tracks two things: data drift (the distribution of inputs has shifted from the training data) and concept drift (the relationship between inputs and outputs has changed). For classification models with ground truth labels, we monitor accuracy, precision, and recall on incoming data. For models where ground truth isn't immediately available, we monitor input feature distributions using statistical tests (KS test, Population Stability Index) and proxy outcome metrics. Evidently AI, whylogs, or custom monitoring in Prometheus — the choice depends on your stack.
- We want to fine-tune an LLM on our proprietary data — what does that involve?
- Fine-tuning requires: a curated training dataset (typically 1,000–100,000 high-quality examples in instruction-response format), infrastructure for the training job (GPU instances on AWS, Azure, or GCP), a training framework (Axolotl, Hugging Face Trainer, or provider-managed fine-tuning APIs like OpenAI or Anthropic), and an evaluation suite to verify the fine-tuned model performs better than the base model on your tasks. We assess whether fine-tuning is actually the right solution first — RAG + prompt engineering is cheaper and faster for many use cases that teams assume require fine-tuning.