Meet the AI Innovators: How Capital One Approaches Data Management and Scaling AI Infrastructure

From startups to enterprise, businesses across industries are collectively coming to a consensus that the time to adopt artificial intelligence (AI) is now. Business leaders are now consuming the best practices for AI adoption, including proper data management – an especially critical focus for the financial services industry. In the second installment of the “Meet the Innovators” webinar series, Capital One’s Zachary Hanif, director at the finance giant’s Center for Machine Learning and principal machine learning engineer, shares his insights on best practices and learnings in data management as it relates to scaling up an AI & machine learning infrastructure.

By watching this webinar replay, you’ll learn:
  1. The definition of “good data” and its importance to any deep learning initiative
  2. Best practices for collecting and managing data
  3. How Capital One leverages AI/machine learning for key use cases such as fraud detection
  4. Why cluster scheduling projects are key to managing scarce physical resources, across both the GPU and CPU spaces
  5. Why considerations like container management, server monitoring, and data storage are critical to project success
  6. The importance of pipeline, data schemas, and monitoring material within enterprise machine learning infrastructures
  7. Resources and strategies to ensure strong feature management
Webinar access will be emailed to you.
Presented By
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Zach Hanif
Director, Center for Machine Learning, Capital One

Zachary Hanif is a Director in Capital One’s Center for Machine Learning, an in-house consultancy and center of excellence for machine learning innovation, research, and product delivery across the business, where he leads the team’s anti-money laundering and credit monitoring work. Previously, Zachary was a pre-Series A employee at Endgame, a startup focused on offensive cyber security products. He also worked as Director of Analytics at Novetta, a defense contractor focusing on large-scale network security products. He graduated from the Georgia Institute of Technology, and spent time afterwards developing and applying large-scale machine learning models to solve cyber security research problems within the Georgia Tech Research Institute.
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Tony Paikeday
Director of Product Marketing, Artificial Intelligence and Deep Learning Systems, NVIDIA

With over 25 years of experience in product management and marketing, business process, and manufacturing engineering, Tony helps organizations infuse the power of deep learning to solve their most important business transformation opportunities. Tony holds an engineering degree from the University of Toronto.
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