Machine learning measurement and validation in the real world

IDEAS & Data Application Lab co-host this live webinar.

国际数据科学与工程协会 IDEAS

IDEAS is a global nonprofit organization that is dedicated to fostering the data engineering and data science ecosystems and broadening the adoption of their underlying technologies to accelerate the innovations data can bring to society. Our goal is to create a community to connect AI and Data Science enthusiasts. All of the conferences that IDEAS host will demonstrate cutting-edge technology and feature a variety of AI and Data Science experts covering topics including industry trends, real-world applications, open-source software, solutions-based case studies, and many others.

Guest Speakers: Robin Way

Topics: Machine learning measurement and validation in the real world

Description: Data science and machine learning practitioners are waking up to the stark reality that a “99% accurate” AI model isn’t what most of us can expect in the real world. What you build in the lab might not generalize to the field. The data you used in development isn’t accessible to your model in production. The fundamental shape of your ML model drivers changes over time, leading to predictive degradation.

The financial services industry is the domain where probability and risk were coined in the 1700s. In this industry, when data science practitioners make these mistakes, tens to hundreds of millions of dollars are at stake, consumers’ credit records can be affected, and heads roll. For these reasons, the financial services industry has put in place repeatable practices to measure model effectiveness and to validate all aspects of these models in ways that is transparent, auditable and explainable.

In this deeply technical and practical discussion, you will learn the practices specific to the best-in-class model performance and validation practices used by financial services practitioners. These practices are completely transferable to virtually every other industry, and the majority (by volume) of actionable business problems, that draw on machine learning approaches. By better understanding how to measure the performance of your models, how to diagnose why they’re working well or poorly, and what you need to do to fix it, you will be able to more effectively set the expectations of your stakeholders, and save yourself a ton of headaches.

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