For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in …
For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in …
This is a guest post by Dr. Naoki Okada, Lead Data Scientist at BrainPad Inc.
Founded in 2004, BrainPad Inc. is a pioneering partner in the field of data utilization, helping companies create business and improve their management through the …
Healthcare data is complex and siloed, and exists in various formats. An estimated 80% of data within organizations is considered to be unstructured or “dark” data that is locked inside text, emails, PDFs, and scanned documents. This data is difficult …
Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers, and stop suspicious payment transactions. Detecting fraud closer to the time of fraud occurrence is key …
This post is co-authored with Hernan Figueroa, Sr. Manager Data Science at Marubeni Power International.
Marubeni Power International Inc (MPII) owns and invests in power business platforms in the Americas. An important vertical for MPII is asset management for renewable …