What are MLOps and LLMOps?
MLOps is the operational discipline of deploying, monitoring, and maintaining machine-learning models in production — covering training pipelines, model registries, deployment patterns, drift detection, and retraining triggers. LLMOps applies the same discipline to large language models — adding evaluation harnesses, prompt versioning, RAG pipeline observability, and cost / latency monitoring. Without MLOps/LLMOps, models go from "working in the notebook" to "silently failing in production."
What does Decision Foundry's MLOps / LLMOps service include?
Model deployment architecture (real-time inference, batch scoring, embedded predictions); MLflow / Vertex AI / SageMaker / Databricks ML platform setup; training pipeline orchestration; model registry and versioning; drift and performance monitoring; A/B and shadow deployment patterns; LLM-specific evaluation harnesses (LangSmith, Langfuse, custom); RAG pipeline observability; and the governance layer enterprise customers need (audit logs, AI usage policy, access controls).
How is this different from data science or AI consulting?
Data science / AI consulting designs and trains models. MLOps / LLMOps operationalizes them — gets them into production, keeps them performing, and shuts them down safely if they degrade. Most failed AI projects don't fail at the model — they fail at the operational layer: data drifts, retraining never happens, latency degrades silently, costs spike unnoticed. We focus on this operational layer; we also do the modelling when needed.
How long does an MLOps / LLMOps engagement take, and what does it cost?
A focused single-model deployment with monitoring (one model, one inference path, drift detection) runs 8–12 weeks. A full ML platform setup with training pipelines, registry, observability, and governance for 5–10 models runs 4–7 months. LLM-specific operationalization (RAG observability, eval harness, prompt versioning) typically adds 4–6 weeks per use case. Discovery call + readiness check first.
We have models in production but no monitoring — where do we start?
This is the most common starting point. We typically begin with an MLOps audit: cataloguing what models exist, where they run, who owns them, and what's monitored (usually very little). Then we retrofit observability and drift detection onto the highest-risk models first — typically 6–8 weeks per model — before designing the broader platform. Crawling before walking is the right call here.
Why Decision Foundry for MLOps and LLMOps?
We've operationalized models across Databricks (Premier Partner — strong MLOps story via Mosaic + MLflow), Snowflake Cortex, Vertex AI, SageMaker, and Azure ML. For LLMs we've shipped production RAG pipelines and agentic AI agents with observability and governance. SOC 2 compliant, GDPR capable — meaning we can ship AI to regulated industries.