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The Fate of Feature Engineering: No Longer Necessary, or Much Easier?

In this contributed article, editorial consultant Jelani Harper believes that features are the definitive data traits enabling machine learning models to accurately issue predictions and prescriptions. In this respect, they’re the foundation of the statistical branch of AI. However, the effort, time, and resources required to engender those features may become obsolete by simply learning them with graph embedding so data scientists are no longer reliant on hard to find, labeled training data.

Molecula Secures $17.6 Million in Series A Funding to Democratize Machine-Scale Analytics and AI

Molecula, an enterprise feature store built for machine-scale analytics and AI, announced it closed a $17.6 million Series A round of funding, bringing its total funding to $23.6 million. The round is led by Drive Capital, with participation from TTV Capital and existing investors including Tensility.

Feature Stores are Critical for Scaling ML Initiatives and Accelerating both Top-line and Bottom-line Impact

Feature stores are emerging as a critical component of the infrastructure stack for ML. They solve the hardest part of operationalizing ML: building and serving ML data to production. They allow data scientists to build more accurate ML features and deploy these features to production within hours instead of months.