Pipeline Step Caching: Don't Recompute What You Don't Have To
Pipeline step caching is memoization for your ML infrastructure, saving time and money by reusing previous results. It's used in MLOps pipelines when inputs and code haven't changed. The footgun: the cache is scoped to one pipeline and a timeout, not globally.
Pipeline step caching avoids re-running expensive jobs by reusing the output of a previous successful run. Think of it as a smart 'skip' button for steps whose inputs and configuration haven't changed. This is crucial in MLOps for iterating quickly, as it lets you rerun a pipeline and only execute the specific steps you've modified, like a new training script. The footgun: caching is off by default and requires a timeout; if the timeout is too short you'll miss valid hits, but if it's too long you might reuse unintentionally stale results.
Read the original → docs.aws.amazon.com
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