AI and machine learning infrastructure
Machine learning infrastructure that turns a model into a dependable product: training pipelines, MLOps, model serving and monitoring on the cloud you already use.
What is machine learning infrastructure?
Machine learning infrastructure is everything that keeps a model working after it leaves the notebook: data and training pipelines, versioning, automated deployment, model serving, and monitoring for accuracy, drift and cost. MLOps is the practice of running that infrastructure reliably, so models can be retrained and released safely.
Updated
What we build
Custom model development
Classifiers, recommenders, vision and forecasting models trained on validated data.
Training pipelines
Automated ingestion, features, training, tuning and validation with versioned experiments.
MLOps
Model registries, CI/CD for models, scheduled retraining and A/B tests.
Serving
Low-latency endpoints, batch jobs and GPU-optimised serving on containers or serverless.
Monitoring
Drift detection, quality tracking and alerts, with dashboards your team can read.
Data engineering
Pipelines, feature stores and warehouses that feed models clean, timely data.
Related client work
Delivered for clients, not demos.
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Assess
Objectives, data and existing systems, with a feasibility check before any spend.
Design
Pipelines, training, serving and monitoring in one architecture and a phased plan.
Build and train
Tracked experiments until the model meets the agreed benchmark.
Deploy and monitor
CI/CD, load tests, drift detection and retraining triggers.
Tools we work with
Chosen per project; we are not tied to one provider.
Questions about AI and ML infrastructure
Which cloud do you work on?
AWS, Google Cloud and Azure. We build on the platform you already use, inside your own account, so billing, security policies and data residency stay under your control. The setup is written as infrastructure code, so it can be reviewed and reproduced.
Do we need MLOps for one model?
If the model affects customers or revenue, yes. At minimum you need versioned data and models, monitoring for accuracy and drift, and a safe way to retrain and roll back. Without these, a model quietly degrades and nobody notices until results suffer.
Can you take over an existing model?
Yes. We start with an audit of the data, training code and serving setup, then stabilise what is already there with tests, monitoring and reproducible builds. Only once the model is dependable do we extend it with new features or retraining.
Related services
Tell us what you want to build
A free 30-minute scoping call with the engineers who would build it. You leave with a clear next step, whether or not we work together.