The potential for machine learning to transform data-intensive businesses is undeniable, but realizing this potential requires more than just an investment in technology.

The data lakehouse architecture is leading this change—particularly for machine learning (ML) and advanced analytics—by combining the strengths of both data lakes and data warehouses.

By applying machine learning algorithms like XGBoost and AutoEncoders, Jeevan developed models capable of identifying fraudulent activities in real-time.

Multimodal Machine Learning Applications Businesses can now incorporate multimodal machine learning to analyze diverse data formats like text, images, and video at the same time.

The 10 hottest data science and machine learning tools include MLflow 3.0, PyTorch, Snowflake Data Science Agent and TensorFlow.

Chandra Madhumanchi stands out as a pioneering force in the fields of machine learning (ML), artificial intelligence (AI), and data engineering. Chandra's leadership role is marked by innovation ...

A new scientific machine learning framework developed by Professors Horacio D. Espinosa, Sridhar Krishnaswamy, and collaborators accurately predicts and inversely designs the mechanical behavior of ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research ...