Free Early Release of Chapters 1 – 4
We’re excited to share the first four chapters of our O’Reilly book, Automating Data Quality Monitoring at Scale.
Chapters 1-3 explain the importance of discovering and resolving data quality issues with machine learning, and how to determine the ROI for your organization. In Chapter 4, we’ll share a variety of techniques to ensure your notifications are empowering your team to fix data quality issues. You’ll discover the critical role notifications play in any data quality issue response.
What information should notifications contain in order to be actionable
How machine learning can prevent alert fatigue by sending the right alerts to the right audience
How thoughtful automation can enable root cause analysis and triage directly from the notification
Co-Founder and CTO at Anomalo
Prior to Anomalo, Jeremy was the VP of Data Science at Instacart, where he led machine learning and drove multiple initiatives to improve the company’s profitability. He’s applied machine learning and AI technologies to everything from insurance and accounting to ad-tech and last-mile delivery logistics.
Previously, he led data science and engineering at other hyper-growth companies like Sailthru. He’s also a recognized thought leader in the data science community with hugely popular blog posts like Deep Learning with Emojis (not Math). Jeremy holds a BS in Mathematics from Wichita State University and an MBA from Columbia University.
Head of Content at Anomalo
Paige has worked with clients such as Airbnb, Grammarly, and Samsara, as well as successful startups like CodeSignal, Tecton, Clerky, and Fiddler.
She specializes in communicating complex software engineering topics to a general audience and has spent her career working with machine learning and data systems, including 5 years as a product manager on Google Search. She holds a joint BA in Computer Science and English from UC Berkeley.