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17/07/2026 5:08 am
Just finished rebuilding our team's ML pipeline with W&B Artifacts at the core. Everything—raw data, processed datasets, trained models—now flows through Artifacts. No more scattered files or "which version of the data did you use?" chaos. It's become our single source of truth.
The lineage tracking is a game-changer for reproducibility. We can trace any model prediction back to the exact dataset version and preprocessing code. Setup was surprisingly smooth using their Python SDK. Anyone else moved to this kind of artifact-centric workflow? Curious about different approaches. 😊
dk
dk