Cloud Infrastructure, Databases & AI Pipelines That Stay Up
Apps and AI models that work on a laptop often fail in production: slow, manual deployments, surprise cloud bills, fragile databases and models nobody monitors.
Reliable, cost-aware infrastructure — automated deployments, well-designed databases, monitored AI pipelines and documentation your team can run with.
Typical timeline: 1-6 Weeks.
Capabilities
- Cloud architecture on AWS, Azure & Google Cloud
- CI/CD pipelines, Docker & Kubernetes
- Database design, migration & tuning (PostgreSQL, MongoDB, Redis)
- Data & AI pipelines: ingestion, training, deployment (MLOps)
- Model serving, monitoring & retraining
- Cost optimisation, backups & security hardening
What you receive
- Infrastructure as Code (Terraform)
- CI/CD Pipelines
- Monitoring & Alerting Dashboards
- Runbooks & Handover Documentation
Technology
AWS · Azure · Google Cloud · Docker · Kubernetes · Terraform · GitHub Actions · PostgreSQL
Frequently asked questions
- Can you take over and fix our existing cloud setup?
- Yes. We start with an audit of your infrastructure, costs and security, then fix the highest-impact issues first — without downtime wherever possible.
- What is MLOps, and do we need it?
- MLOps is the practice of deploying, monitoring and updating machine learning models reliably. If a model affects your business every day you need at least monitoring and a retraining path; we set up as much as your use case needs, and no more.
- Which cloud should we use?
- Whichever fits your team, compliance needs and budget. We work across AWS, Azure and Google Cloud, and can also deploy on-premise where data has to stay on your own servers.
- Who owns the infrastructure?
- You do. Everything runs in your own cloud accounts, defined as code, with credentials and documentation handed over.
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