Machine Learning Governance for Managers provides readers with the knowledge to unlock insights
from data and leverage AI solutions. In today's business landscape most organizations face
challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book
offers a comprehensive framework that covers business requirements data generation and
acquisition modeling model deployment performance measurement and management providing a
range of methodologies technologies and resources to assist data science managers in adopting
data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly
detailing skills and techniques to ensure the right things are measured and acted upon for
reliable results and high performance.Readers will learn sustainable tools for implementing
machine learning with existing IT and privacy policies including versioning all models
creating documentation monitoring models and their results and assessing their causal
business impact. By overcoming these challenges bottom-line gains from AI investments can be
realized. Organizations that implement all aspects of AI ML model governance can achieve a high
level of control and visibility over how models perform in production leading to improved
operational efficiency and a higher ROI on AI investments. Machine Learning Governance for
Managers helps to effectively control model inputs and understand all the variables that may
impact your results. Don't let challenges in machine learning hinder your organization's growth
- unlock its potential with this essential guide.
