How to explain the machine learning life cycle to business execs
By Isaac Sacolick, contributing writer for Infoworld.com
If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops. If you have ML models running in production, you probably use ML monitoring to identify data drift and other model risks.
This AI model can predict the characteristics of the river water
By Asif Razzaq, contributing writer for Marktechpost.com
The Susquehanna River spans over 27,510 square miles across New York, Pennsylvania, and Maryland, providing important natural and recreational resources for local communities. The Susquehanna River Basin Commission stewards the river basin’s resources, which support millions of people and a variety of economies, including agriculture and tourism. However, despite collecting data at multiple sites along the river basin and using a data-driven approach, SRBC required a model-driven approach to predict the water characteristics, such as specific conductance of the river water.
New ML method improves our understanding of cell identity
By Carnegie Melon University, contributing writers for Scitechdaily.com
Activation and expression of genes reveal similarities in cell patterns based on type and function throughout the tissues and organs. Understanding these patterns improves our comprehension of cells and offers insights into uncovering the underlying mechanisms of diseases.
AI and machine learning in wealth management: Customized Portfolios, predictive analytics
By Finance Magnates Staff, contributing writers for Financemagnates.com
Wealth management is a complex and constantly evolving field, with a vast amount of data to analyze and complex decisions to make. With the rise of artificial intelligence (AI) and machine learning (ML), the field of wealth management has experienced a significant transformation in recent years.
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