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 modeling 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: cataloging 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.