Deployment Pipelines and Evaluation Frameworks
We implement CI/CD for ML and LLM workloads, including model registries, prompt libraries, evaluation pipelines, and controlled promotion from development to production.
From Notebook to Production — And Staying There Reliably
Getting a model to work in development is the easy part. Getting it to run reliably in production — with real users, real volumes, and real expectations — requires a different set of disciplines entirely. Without MLOps and LLMOps, AI exists only in controlled environments and degrades quietly without warning.
AAL builds the deployment pipelines, evaluation frameworks, monitoring stacks, and governance controls that make AI a reliable operational capability. We cover model CI/CD, prompt and version management, drift monitoring, observability, human feedback loops, rollback controls, guardrails, and cost optimisation — across both ML and LLM workloads.
- Model deployment pipelines with CI/CD, approval gates, and rollback controls
- Prompt and version management with regression testing and A/B capability
- Drift monitoring, observability, and quality evaluation pipelines
- Inference optimisation, cost management, and cloud spend visibility
Our Service Benefits
Organisations that invest in MLOps and LLMOps infrastructure ship AI faster, maintain quality more reliably, and operate at lower cost per outcome. The platform becomes a competitive advantage as the volume and diversity of AI deployments grows.
Observability, Guardrails, and Cost Control
We set up monitoring for quality, drift, latency, and spend — and implement guardrails and rollback controls so production AI is safe to operate and maintain.