Model interpretation for businesses? with David Moskowitz at Crane Co
The Data Standard
To either a model-driven company or a company catching up with the rapid adoption of AI in the industry, machine learning model interpretation has become a key factor that helps to make decisions towards promoting models into business. This is not an easy task — imagine trying to explain a mathematical theory to your parents. Yet business owners should always be curious about these models, and some questions easily raise:
- How do machine learning models work?
- How reliable are the predictions made from these models?
- How carefully do we have to monitor these models?
Meet The Host
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Data Analytics Lead and Head of Analytics Steering Committee, Global Internal Audit at Crane Co.
A data analyst, Blockchain enthusiast, and lifelong learner with the goal of using data analytics to optimize marketing, business decisions, and accounting analytics for the company I work for. I currently lead the digital transformation and data analytics steering committee of internal audit for Crane Co, a global publicly traded manufacturing firm. My go to tools are SQL, Python, Alteryx, and Power BI. With years of data analytics and ETL methods coupled with client facing experience (Fortune 500 and small firms), I communicate effectively across departments within an organization, especially accounting and audit groups. More importantly, I translate complex data and methods in a clear, down to earth way for people of all backgrounds to understand.