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:
- The definition of “good data” and its importance to any deep learning initiative
- Best practices for collecting and managing data
- How Capital One leverages AI/machine learning for key use cases such as fraud detection
- Why cluster scheduling projects are key to managing scarce physical resources, across both the GPU and CPU spaces
- Why considerations like container management, server monitoring, and data storage are critical to project success
- The importance of pipeline, data schemas, and monitoring material within enterprise machine learning infrastructures
- Resources and strategies to ensure strong feature management