The full answer
Many AI efforts stall after a successful notebook or demo because nothing is in place to run the model reliably, watch it in production or update it safely. MLOps closes that gap with pipelines that train, test and deploy models in a repeatable way, with every version recorded.
For predictive models, Creuto sets up data pipelines, model registries, automated evaluation against held-out data, containerised serving through FastAPI, drift monitoring and scheduled or triggered retraining. Infrastructure is defined with Terraform and deployed through CI/CD.
For LLM applications the moving parts differ: prompts, retrieval settings, model versions and tool definitions. LLMOps versions all of these, runs evaluation sets on every change, logs requests and responses with redaction, tracks token cost and latency per feature and supports gradual rollouts with rollback.
The result is that an AI feature can be changed with confidence. When a provider releases a new model, the team can test it against the evaluation set and switch in a controlled way instead of hoping for the best.
Creuto can set this up for products it builds or for an existing AI system that has grown without it, and hands the pipelines over with documentation.