AI, ML, MLOps and AIOps
The infrastructure and pipelines that take machine learning from a notebook to a monitored production service, and AI applied where it measurably helps.
Overview
Most of the effort in production machine learning is engineering: reliable data pipelines, reproducible training, model packaging, serving infrastructure and monitoring. That is where we focus.
We build MLOps pipelines that version data and models, automate evaluation and promote models through environments with the same discipline as application code. Serving is designed around the latency, throughput and cost your use case actually needs.
On the operations side, AIOps can cut alert noise and spot anomalies earlier, but only when the underlying telemetry is sound. We start there, and we are clear about what AI can and cannot reliably do for your workflow.
Problems we address
- Models that work in experiments but never reach production
- No record of which model version is serving or how it was trained
- GPU or compute spend with no link to business value
- Model quality degrading silently as input data changes
- Operations teams overwhelmed by alerts that do not indicate real problems
What we deliver
- ML infrastructure
- Training and serving environments on Kubernetes or managed cloud services, sized for the workload.
- Model deployment and serving
- Containerised inference services with autoscaling, canary rollout and rollback.
- MLOps pipelines
- Automated training, evaluation, registry and promotion with traceable lineage.
- Model monitoring
- Latency, error, drift and quality signals with clear ownership of alerts.
- AI-enabled workflows
- Document processing, classification and assistance features integrated into existing systems.
- AIOps
- Anomaly detection, alert correlation and operational insight built on clean telemetry.
- AI-ready data pipelines
- Ingestion, validation and feature preparation that models can depend on.
Technology
Typical tools for this work. We choose per engagement; listing is not a claim of vendor partnership.
- Python
- Kubernetes
- KServe
- MLflow
- Azure Machine Learning
- Amazon SageMaker
- Vertex AI
Example use cases
Illustrations of the kind of work this covers.
- Automating extraction and classification of business documents
- Serving a trained model behind an API with monitoring and safe rollout
- Detecting unusual patterns in transaction or operational data
- Reducing duplicate and low-value alerts in an operations team
Relevant industries: Finance and Financial Services
Delivery approach
Discover
Understand the business goal, the systems in place and the constraints: budget, compliance, skills and timelines.
Design
Agree an architecture and delivery plan, with the trade-offs written down and the risks named early.
Build
Deliver in small, reviewed increments with automated tests, infrastructure as code and regular demonstrations.
Launch
Release through pipelines with monitoring, rollback paths and runbooks in place before go-live.
Operate and improve
Run, measure and refine, or hand over fully documented to your own team.
Related services
- Data Engineering and Database ManagementDatabase architecture, migration, performance and data pipelines across MySQL, PostgreSQL, MongoDB and Azure Cosmos DB.
- DevOps and Platform EngineeringCI/CD, GitOps, Kubernetes, OpenShift and service mesh engineering that makes releases routine and environments consistent.
- API and Integration ManagementAPI design, gateways, authentication and event-driven integration that connect your systems without turning them into a tangle.
Talk to us about ai & mlops
Share the problem, the systems involved and your timeline. An engineer will reply to discuss scope and next steps.
Request a consultation