Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.
MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.
The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
.svg)
.svg)
.svg)
.svg)