The distance between a notebook and a dependable service is called MLOps. In 2026 the practices are mature; the differentiator is discipline.
The core loop
Version data, code, and models together; promote through automated evaluation gates; deploy behind canary releases; monitor prediction quality and input drift; retrain on a trigger, not a calendar.
LLMOps additions
Prompt versioning, evaluation suites with golden datasets, token-cost dashboards, and guardrail testing join the classical stack when language models enter production.
Start smaller than you think
One model, one pipeline, full automation — then replicate the pattern. Datastrel stands up pragmatic MLOps on AWS (SageMaker, Bedrock, Glue) sized for teams of five, not five hundred.