mlops2.md

Challenge	Automation solution	Keep It Simple Solution
Training-Serving Skew, where differences crop up between training and serving stacks	Feature Store	Transform Pattern
Clients are interested in a capability that involves multiple steps, only one of which is the ML model	ML Pipelines	Stateless Serving Function
The incoming data changes characteristics	Continuous evaluation	Scheduled evaluation
The correct answer changes over time	Continuous training	Systematic triggers
Model fairness issues arise	Mumbo Jumbo	Manual sliced evaluations and other checks
Parts of the system aren’t changed when new code is checked in	CI/CD	From-scratch builds
Need to be able to troubleshoot old results	Model registry, Data registry	Version control all deployed artifacts