AI Engineering
Production ML systems built for scale and reliability.
We engineer end-to-end machine learning systems — from data pipelines and model training to serving infrastructure and monitoring — that perform reliably in production under real-world load, not just in notebooks.
The Gap Between a Model and a Product
Most AI projects fail not because the model is bad, but because of everything around it. The data pipeline breaks. The model drifts silently over months. Inference is too slow. The serving infrastructure cannot handle a traffic spike. No one thought to version the training data.
AI Engineering is the discipline of closing that gap — building all the infrastructure, tooling, and practices required to get a machine learning model from a research prototype into a production system that your users can rely on.
At CodeWingz, AI engineering is not a separate team — it is baked into how we approach every ML project. Every model we build ships with a data pipeline, a retraining mechanism, a serving API, a monitoring dashboard, and a rollback strategy. We treat your ML system with the same engineering rigour we apply to any critical backend service.
Service Inclusions
ML Pipelines
Automated, reproducible data pipelines with validation, versioning (DVC), and scheduling — so model training is a reliable, auditable process, not a manual exercise.
Model Serving Infrastructure
REST and gRPC serving via FastAPI, TorchServe, or BentoML with autoscaling, load balancing, and A/B model routing for canary deployments.
MLOps & CI/CD
GitHub Actions or GitLab CI pipelines for automated model training, evaluation gating, Docker image building, and staged production deployment on every code push.
Drift Detection & Monitoring
Evidently AI or custom monitoring for data drift, prediction drift, and model performance degradation — with Slack/PagerDuty alerting before users notice degraded outputs.
Feature Stores
Feast or Tecton feature stores for consistent, low-latency feature serving across training and inference — eliminating training/serving xkew and speeding up experimentation.
Experiment Tracking
MLflow or Weights & Biases experiment tracking with full hyperparameter logging, artifact versioning, and team collaboration — so every model decision is documented and reproducible.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Codewingz follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Architecture Design
We review your existing ML setup, identify technical debt, and produce a production architecture document covering data flow, model serving, monitoring, and retraining strategy.
Data Pipeline Build
Automated ingestion, validation, transformation, and versioning pipeline. Schema enforcement and data quality checks at every stage.
Training Infrastructure
Cloud GPU training environment setup, experiment tracking integration, and automated hyperparameter optimisation. Reproducible training runs from a single configuration file.
Serving & API Layer
Model exported to production format, wrapped in a FastAPI service, containerised with Docker, and deployed with autoscaling. Load tested to your traffic projections.
Monitoring & Alerting
Prediction and data drift monitors deployed. Grafana dashboard for model performance metrics. PagerDuty integration for on-call alerting.
CI/CD & Handover
Full MLOps pipeline connected to your Git workflow. Automated retraining triggers. Team documentation and handover session.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
Kubernetes
Orchestrating containerized applications.
MLflow
Open source platform for the ML lifecycle.
DVC
Data Version Control for ML projects.
Terraform
Infrastructure as Code to automate cloud resources.
Evidently AI
Evaluate and monitor ML models in production.
BentoML
Unified model serving framework.
Real-World Impact
LogiRoute
The Challenge
“A logistics SaaS company had a demand forecasting model trained in a notebook by a data scientist who had since left. The model was retrained manually every quarter, had no monitoring, and had silently degraded after a supply chain disruption changed the underlying data distribution — causing costly over-stocking recommendations for 6 months before it was discovered.”
The Solution
We rebuilt the demand forecasting system as a proper ML product: automated weekly retraining pipeline (Airflow + DVC), Evidently AI monitoring catching data and prediction drift within 24 hours, A/B model deployment routing 10% of traffic to new model versions before full rollout, and a FastAPI serving layer handling 50k predictions/day with 95ms median latency.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Time to Take Your Models to Production?
Whether you have a notebook, a prototype, or a broken production system — we will scope the work needed to make your ML reliable, observable, and maintainable.