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MLOps & AI Platforms That Keep AI Running
Models only create value when they run reliably. We build the pipelines, platforms and monitoring that take AI from notebooks to dependable production services.
Quick answer
What is MLOps?
MLOps (machine learning operations) is the practice of building, deploying, monitoring and updating machine learning and AI models reliably, much like DevOps for software. ML Data House sets up automated pipelines, model registries, CI/CD, monitoring, drift detection and LLMOps tooling on AWS, Azure or Google Cloud.
- Automated training and deployment pipelines
- Model registry, versioning and rollback
- Monitoring for accuracy, drift, latency and cost
- LLMOps: prompt versioning and evaluation
What we deliver
What our MLOps & AI Platforms service covers
ML pipelines
Repeatable data prep, training and validation pipelines.
CI/CD for ML
Automated tests and safe deployment of models and prompts.
Feature and data management
Feature stores, data versioning and lineage.
Monitoring
Accuracy, drift, latency, errors and cost in one view.
LLMOps
Prompt management, evaluation sets, tracing and guardrails for LLM apps.
Platform engineering
Kubernetes, serverless and GPU infrastructure sized for your needs.
How it works
Our MLOps & AI Platforms process
Short, visible steps with a demo or decision at the end of each one.
Design
Platform architecture and target workflow.
Build
Pipelines, registry, CI/CD and monitoring.
Enable
Migrate first models and train your team.
Deliverables
What you get
- MLOps platform architecture
- Automated training and deployment pipelines
- Model registry and versioning
- Monitoring and alerting dashboards
- Runbooks and team training
Best fit
This is right for you if…
- Models live in notebooks or on one laptop
- Deployments are manual and risky
- Nobody knows when a model stops working
- LLM apps have no evaluation or tracing
UK & US
MLOps & AI Platforms for UK and US businesses
One remote team, planned around the rules and working patterns of each market.
United Kingdom
We work from our UK office in Wolverhampton with clients across England, Scotland, Wales and Northern Ireland, remotely. Projects are planned around UK GDPR and the Data Protection Act 2018, ICO guidance on AI and data protection, and, if you sell into Europe, the EU AI Act.
For UK clients we keep data and models in UK or EU regions and keep lineage records that support UK GDPR accountability.
United States
We work remotely with US companies from coast to coast, and UK working hours overlap with the US East Coast morning for live calls and demos. We plan around the NIST AI Risk Management Framework, state privacy laws such as the CCPA/CPRA, and sector rules like HIPAA where they apply.
For US clients we use US regions and keep audit trails that support SOC 2 and internal model-risk reviews.
Tools, models & frameworks
MLflowKubeflowAirflowDVCDockerKubernetesAWS SageMakerAzure MLVertex AILangSmithPrometheusGrafana
Keep exploring
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FAQ
MLOps & AI Platforms: frequently asked questions
What is the difference between MLOps and DevOps?
DevOps manages code. MLOps also manages data and models, which change over time and need retraining, validation and monitoring for drift.
What is LLMOps?
LLMOps applies MLOps ideas to large language model applications: versioning prompts, evaluating answers, tracing calls, applying guardrails and tracking cost.
What is model drift?
Model drift is when a model becomes less accurate because the real world or the incoming data changes. Monitoring detects it so the model can be retrained.
Which cloud platforms do you use?
AWS, Microsoft Azure and Google Cloud, and on-premise Kubernetes where needed.
Do we need MLOps for one model?
A light version, yes: automated deployment, versioning and monitoring save time and prevent silent failures even for a single model.
Can you improve our existing ML platform?
Yes. We review what you have and fix the gaps rather than replacing working tools.
How does MLOps relate to your ML development work?
Every model we build through machine learning development is deployed with MLOps practices.
Ready to talk about MLOps & AI Platforms?
Tell us about your goals, data and systems. We will reply with practical next steps, wherever you are in the UK, the US or worldwide.