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Travis CI build stages let you gate deploys behind tests with a simple stages/jobs.include syntax. Here's a worked three-stage config, plus the artifact-persistence trap most teams hit.
A decision guide showing why wrapping preprocessing and the estimator in a scikit‑learn Pipeline with ColumnTransformer prevents data leakage, how to choose scaling, encoding, and imputation strategies per model type, and how to validate the setup with cross‑validation and persistence checks.
Learn how to turn application error logs into a Datadog log‑based metric using pipelines, with a concrete Python example, verification steps, and key limitations.
Library Overrides let you link a master asset into many shots and edit only what you need—pose, material, transform—while keeping the link alive for future updates. This blog walks through a character‑rig example, highlights trade‑offs, and gives a verification you can run in Blender 3.6 today.
Learn how ShotGrid’s custom entity schema lets you add bespoke fields to assets, shots, or custom entities, drive new API endpoints, and keep your pipeline flexible. A step‑by‑step example shows adding a ‘Story Beat’ list field to shots and querying it via shotgrid3.
Learn how to combine numeric and categorical preprocessing into a single Pipeline to avoid data leakage and simplify model training.
Learn how to combine scikit‑learn's Pipeline with ColumnTransformer to preprocess mixed numeric and categorical data safely and reproducibly.
Master scikit‑learn’s Pipeline and ColumnTransformer: understand trust boundaries, prevent data leakage, and validate your design with practical checks and failure‑mode awareness.
Learn how to wrap preprocessing steps in a Pipeline so that statistics are learned only on training data, avoiding leakage when modeling mixed numeric and categorical features.
datadog
blender
shotgrid