Practical Dataops: Delivering Agile Data Science at Scale

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Practical Dataops: Delivering Agile Data Science at Scale

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內容描述

Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles.
 
This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. 
 
 
What You Will Learn

Develop a data strategy for your organization to help it reach its long-term goals
Recognize and eliminate barriers to delivering data to users at scale
Work on the right things for the right stakeholders through agile collaboration
Create trust in data via rigorous testing and effective data management
Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes
Create cross-functional self-organizing teams focused on goals not reporting lines
Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products

 
Who This Book Is For
 
Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

作者介紹

Harvinder Atwal is a data professional with an extensive career using analytics to enhance customer experience and improve business performance. He is excited not just by algorithms, but also by the people, processes, and technology changes needed to deliver value from data. He enjoys the exchange of ideas, and has spoken at O'Reilly Strata Data Conference London, ODSC London, and Data Leaders Summit Barcelona.
Harvinder currently leads the Group Data function responsible for the entire data life cycle, including: data acquisition, data management, data governance, cloud and on-premise data platform management, data engineering, business intelligence, product analytics, and data science at Moneysupermarket Group. Previously, he led analytics teams at Dunnhumby, Lloyds Banking Group, and British Airways. His education includes an undergraduate degree from University College London and a master's degree in Operational Research from Birmingham University's School of Engineering.