Accelerating the ML Lifecycle with an Enterprise-Grade Feature Store

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Productionizing real-time ML models poses unique data engineering challenges for enterprises that are coming from batch-oriented analytics. Enterprise data, which has traditionally been centralized in data warehouses and optimized for BI use cases, must now be transformed into features that provide meaningful predictive signals to our ML models. Enterprises face the operational challenges of deploying these features in production: building the data pipelines, then processing and serving the features to support production models. ML data engineering is a complex and brittle process that can consume upwards of 80% of our data science efforts, all too often grinding ML innovation to a crawl.

Atlassian will join us to provide first-hand perspective from an enterprise who has successfully deployed a feature platform in production. The platform powers real-time, ML-driven personalization and search services for a popular SaaS application.

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Is it true that only ~750 feature vectors can be pulled at once from Tecton ?
Can we not delete feature vectors from tecton ?
How do we scale Tecton ?

HiteshSethiya

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