AI service
MLOps & LLMOps
Run AI in production with the same discipline as the rest of your software.
Discuss your project
What are MLOps and LLMOps?
MLOps and LLMOps are the practices for deploying, monitoring and updating machine learning and language model systems in production. Unicorn Softech sets up CI/CD for models and prompts, evaluation pipelines, monitoring, and cost and safety controls.
How it works
MLOps & LLMOps in 5 steps
- 01Change
- 02Evaluate
- 03Deploy
- 04Monitor
- 05Improve
What we deliver
What you get with MLOps & LLMOps
Model & prompt CI/CD
Versioned releases with automated evaluation gates and rollback.
Monitoring
Quality, drift, latency and cost tracked in real time.
Evaluation pipelines
Regression tests for AI behaviour on every change.
Model hosting
Self-hosted open-source models on GPUs when privacy or cost demand it.
Safety filters
Prompt-injection defences, PII redaction and content moderation.
Gateway & routing
One entry point for multiple model providers with fallbacks.
Common use cases
- Moving an AI prototype to production
- Reducing LLM spend
- Self-hosting models for data privacy
- Auditable AI for regulated industries
Tools and technology
We choose per project. These are the tools we use most for mlops & llmops.
- MLflow
- Langfuse
- LangSmith
- Docker
Kubernetes- vLLM
- Azure ML
- GitHub Actions
- Model & prompt CI/CD
- Monitoring
- Evaluation pipelines
- Model hosting
- Safety filters
- Gateway & routing
- Moving an AI prototype to production
- Reducing LLM spend
- Self-hosting models for data privacy
- Auditable AI for regulated industries
FAQ
MLOps & LLMOps: common questions
What is LLMOps?
LLMOps is the set of practices for running language-model applications reliably: versioning prompts, evaluating outputs, monitoring cost and quality, and guarding against misuse.
Related
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Let's talk about MLOps & LLMOps
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