MLOps & LLMOps Services
Pipelines that take models from experiment to production and keep them healthy: versioning, automated deployment, rollback and drift checks.
About our MLOps services
Many models never leave the notebook. Others reach production by hand and are hard to update. MLOps brings DevOps habits to machine learning. Our MLOps services build the pipeline that trains, tests, versions and ships your models the same way every time. For LLM applications, LLMOps adds prompt versioning and evaluation. You can change a prompt or a model without guessing what will break.
What's included in our MLOps services
How we deliver MLOps services
We look at how your team works today. Then we automate the slowest manual steps first. Code, data and models are versioned together. Every change runs through tests before it reaches production. New versions roll out gradually and can be rolled back in one step. We use open-source tools that run on your own Kubernetes or cloud, so you are not locked in.
Tools and technology for MLOps services
Outcomes of our MLOps services
- Models reach production faster, and more often
- Every model version traceable to its code and data
- Safe updates with quick rollback
Frequently asked questions about MLOps services
What is the difference between MLOps and DevOps?
DevOps automates how software is built and released. MLOps does the same for machine learning, and adds data and models to the mix. A model can get worse over time even when the code does not change. So MLOps also tracks data versions, model quality and drift.
What is LLMOps?
LLMOps is MLOps for applications built on large language models. It adds prompt versioning, answer evaluation, cost tracking and guardrails. LLM apps change through prompts and model choice as much as through code, so these need the same care.
Do we need Kubernetes for MLOps?
Not always. Kubernetes helps when you serve several models or need to scale. Smaller teams can start with a simpler setup. We design the pipeline so it can move to Kubernetes later.
Related Services
AI Infrastructure Services
GPU servers and Kubernetes clusters for AI training and inference, set up on-premises or in your cloud and handed over ready to run.
AI & GPU Monitoring Services
Monitoring for GPU clusters and AI applications: GPU health and usage, model latency, errors, token use and cost, in one place.
Private LLM Deployment
Open-source large language models hosted in your own data centre or cloud account, with document search (RAG) so answers come from your data.
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