Databricks, the lakehouse company, announced the launch of Databricks Model Serving to provide simplified production machine learning (ML) natively within the Databricks Lakehouse Platform. Model Serving removes the complexity of building and maintaining complicated infrastructure for intelligent applications. Now, organizations can leverage the Databricks Lakehouse Platform to integrate real-time machine learning systems across their business, from personalized recommendations to customer service chatbots, without the need to configure and manage the underlying infrastructure.
Databricks Launches Simplified Real-Time Machine Learning for the Lakehouse
March 7, 2023 by Leave a Comment
The Fate of Feature Engineering: No Longer Necessary, or Much Easier?
April 1, 2021 by Leave a Comment
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
January 17, 2021 by Leave a Comment
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
January 10, 2021 by Leave a Comment
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.
