MLOps: Taking Models to Production
CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.
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Infrastructure
MLOps & LLMOps
Most models either never leave the development environment or quietly degrade once in production, and LLM applications need continuous measurement of quality, cost and safety. We make your models reliable by building an MLOps and LLMOps platform that automates the entire lifecycle, from training and deployment to monitoring and evaluation.
What we offer
- MLOps platform architecture and tool selection
- Experiment tracking, model registry and versioning
- CI/CD pipelines for model training and deployment
- Model monitoring, data drift detection and alerting
- LLMOps: tracing, prompt versioning, automated evaluation and cost tracking
When you need it
- Taking models to production takes weeks or months
- You can't monitor the performance of models or LLM applications in production
- Managing models gets harder as their number grows
How we work
Assess
We review your current ML lifecycle and maturity level.
Architect
We design the platform architecture that fits your needs.
Build
We set up the platform and migrate the first models.
Scale
We train your team and standardise processes.
What you get
- A working MLOps and LLMOps platform
- Automated training and deployment pipelines
- Monitoring dashboards and alerts
- Platform documentation and team training
Related courses
Building LLM Applications
RAG architectures, agents and MCP, output evaluation, security and on-premise LLM deployment.
Learn more →DevOps & AWS Fundamentals
Containers, automation and the fundamentals of AWS cloud architecture.
Learn more →Frequently Asked Questions
Will it integrate with our existing tools?
Yes. The platform is designed around the cloud, code repository, CI/CD and monitoring tools you already use.
Open source or commercial tools?
That depends on needs, budget and existing infrastructure. Widely used open-source tools such as MLflow are often preferred.
Can it support our LLM applications?
Yes. The platform can include LLMOps components such as prompt versioning, tracing and automated evaluation.
Where should we start?
The free MLOps Maturity Assessment is a good way to see your current level. The first step is usually experiment tracking, a model registry and basic monitoring.
Free assessment · 4 min
Start now
MLOps Maturity Assessment
Measure the MLOps maturity of your model development and deployment in 12 questions; see your level (0–4) and next steps.
Related articles
MLOps Maturity Levels: A Roadmap from Level 0 to 4
MLOps maturity determines how fast and reliably your models reach production. The levels in Microsoft's and Google's models, what characterises each level and the next step.
Read more →What Is MLOps? Why Models Never Reach Production
MLOps is the discipline of getting machine learning models into production reliably, and keeping them there. Its components, maturity levels, LLMOps and how it differs from AIOps.
Read more →Deploying LLMs On-Premise: Hardware, Model Choice and Cost
You can use large language models without your data leaving the building. Model choice, GPU sizing, serving software, security and the cost case for on-premise LLM deployment.
Read more →Let's talk about your project
Tell us about your training, consulting or software needs and we will get back to you within 2 business days.