ISBN: 9781098145934 - automating data quality monitoring at scale: scaling beyond rules with machine learning (28 resultados)

Condición: New.

Condición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Paperback or Softback. Condición: New. Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning. Book.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Condición: New. The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.This book will help you:Learn why data quality is a business imperativeUnderstand and assess unsupervised learning models for detecting data issuesImplement notifications that reduce alert fatigue and let you triage and resolve issues quicklyIntegrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systemsUnderstand the limits of automated data quality monitoring and how to overcome themLearn how to deploy and manage your monitoring solution at scaleMaintain automated data quality monitoring for the long term.…

Paperback. Condición: new. Paperback. The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.This book will help you:Learn why data quality is a business imperativeUnderstand and assess unsupervised learning models for detecting data issuesImplement notifications that reduce alert fatigue and let you triage and resolve issues quicklyIntegrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systemsUnderstand the limits of automated data quality monitoring and how to overcome themLearn how to deploy and manage your monitoring solution at scaleMaintain automated data quality monitoring for the long term In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: new.

Condición: New.

Condición: New. 2024. 1st Edition. paperback. . . . . .

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New.

Paperback. Condición: Brand New. 170 pages. 9.19x7.00x0.46 inches. In Stock.

Condición: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Condición: New.

Condición: NEW.

Taschenbuch. Condición: Neu. Neuware - The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: - Learn why data quality is a business imperative - Understand and assess unsupervised learning models for detecting data issues - Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly - Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems - Understand the limits of automated data quality monitoring and how to overcome them - Learn how to deploy and manage your monitoring solution at scale - Maintain automated data quality monitoring for the long term.…

Condición: New. &Uumlber den AutorJeremy 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. Pr.

Paperback. Condición: new. Paperback. The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.This book will help you:Learn why data quality is a business imperativeUnderstand and assess unsupervised learning models for detecting data issuesImplement notifications that reduce alert fatigue and let you triage and resolve issues quicklyIntegrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systemsUnderstand the limits of automated data quality monitoring and how to overcome themLearn how to deploy and manage your monitoring solution at scaleMaintain automated data quality monitoring for the long term In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: New. The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.This book will help you:Learn why data quality is a business imperativeUnderstand and assess unsupervised learning models for detecting data issuesImplement notifications that reduce alert fatigue and let you triage and resolve issues quicklyIntegrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systemsUnderstand the limits of automated data quality monitoring and how to overcome themLearn how to deploy and manage your monitoring solution at scaleMaintain automated data quality monitoring for the long term.…

Taschenbuch. Condición: Neu. Automating Data Quality Monitoring | Scaling Beyond Rules with Machine Learning | Jeremy Stanley (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | O'Reilly Media | EAN 9781098145934 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Paperback. Condición: Brand New. 170 pages. 9.19x7.00x0.46 inches. In Stock. This item is printed on demand.