Ideas followed across the years
Follow a subject through years of MLOps Community sessions: how people approached it, what they tried and where they disagreed. New sessions join each thread as the archive grows.
The archive by year
Session counts and the most common topic tags for each year.
How do you tell whether a model still works once people depend on it? Speakers look for answers in drift statistics, production traces and evals, including the disputed practice of asking one model to judge another.
Feature stores promised to fill a gap in the ML platform.
Who builds the platform data scientists use, and how big should that team be? Then comes the recurring discovery that building it does not make anyone use it.
Cost becomes a much more common topic in the archive in 2023.
What data can a model use, and what should it be allowed to do? Privacy and governance discussions meet newer problems with prompt injection and agent permissions.
People label the training data and review LLM output.
The mechanics of getting a model to answer requests, from Flask apps and Kubernetes to inference servers and hosted model APIs.
The oldest complaint in the archive, restated for every new kind of model.
What can an agent do on its own, and which tools should it get? Once it starts acting, someone needs a way to find out when it goes wrong.
When is a fine-tuned open model a better fit than a hosted API?