Ryohei Fujimaki, PhD | dotData CEO – discusses five key factors why white-box data science models are superior to black-box models for deriving business value from data science. Read the full article on e-Week: Why White-Box Models in Enterprise Data Science Work More Efficiently
- April 30, 2019
As automation increases and business needs evolve, so will the ranks of data scientists. But while the future data scientist role may look a little different — with a heavier focus on business operations and oversight — it will be no less important to enterprises.
“As the adoption of automated machine learning platforms … spreads, the role of the data scientist will become less about building models and more about implementing them in a meaningful way,” said Forrester analyst Brandon Purcell.
Read the full article “A future data scientist needs business, deep learning skills” in the TechTarget featuring Ryohei Fujimaki, dotData CEO and founder.
- Carl Bowen
- White Papers
- November 13, 2018
Our white paper – Three Reasons You Need White-Box Models in Enterprise Data Science is now available. Download it free, compliments of dotData.
HIGHLIGHTS FROM THE REPORT:
An overview of what you should know about your enterprise data science / white-box models, including:
- Making the Move to White-Box Models in Data Science
- White-Box vs. Black-Box Models
- Why a White-Box Models Matters
Learn more about dotData, and discover why Forrester said that “dotData is AutoML’s best kept secret” and should make enterprise shortlists for comprehensive AutoML solutions – download your free copy today.