Automating Data Quality Monitoring at Scale - Scaling Beyond Rules with Machine Learning

"This practical book shows you how to ensure that the data your organization relies on is trustworthy ... [and] explain[s] how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on issues, and resolve problems immediately"--

About the author

Jeremy Stanley is 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. Previously, he led data science and engineering at other hyper-growth companies like Sailthru. He's applied machine learning and AI technologies to everything from insurance and accounting to ad-tech and last-mile delivery logistics. 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.

Product details

• Authors: Paige Schwartz, Jeremy Stanley
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 29.12.2023
• EAN: 9781098145934
• ISBN: 978-1-09-814593-4
• Dimensions: 178 mm x 233 mm x 15 mm
• Weight: 666 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; BUSINESS & ECONOMICS / Management, Data Warehousing, information architecture, Management and management techniques, Database design and theory, COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Data Warehousing, Database design & theory, Software testing and verification, data quality data observability data learning