Build a library of great features. Serve them in production. Do it at scale.
Models can only be as good as the features they consume, and features can only be as good as the raw data they consume. Whether precomputed in batch, or generated in real-time, derive the highest quality signal from the company’s best data.
Features shouldn’t live in artificial silos. They should be discoverable and available for use across the company. Curate features in a centralized feature store. No more silos, no more duplication.
Features are business-critical building blocks of any ML application. They should be treated with the same automation and standards as production code. Deploy features quickly and serve them at scale with enterprise SLAs.
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