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Practical DataOps: Delivering Agile Data Science at Scale de Harvinder Atwal

Practical DataOps: Delivering Agile Data Science at Scale

Autor Harvinder Atwal
en Limba Engleză Paperback – 10 dec 2019
Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making.

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.
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Specificații

ISBN-13: 9781484251034
ISBN-10: 1484251032
Pagini: 240
Ilustrații: XXVIII, 275 p. 43 illus.
Dimensiuni: 178 x 254 x 29 mm
Greutate: 0.58 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

Part I. Getting Started.- 1. The Problem with Data Science.- 2. Data Strategy.- Part II. Toward DataOps.- 3. Lean Thinking.- 4. Agile Collaboration.- 5. Build Feedback and Measurement.- Part III. Further Steps.- 6. Building Trust.- 7. DevOps for DataOps.- 8. Organizing for DataOps.- Part IV. The Self-Service Organization.- 9. DataOps Technology.- 10. The DataOps Factory.

Notă biografică

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.

Textul de pe ultima copertă

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making.

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. 

You will:
  • 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 andeffective 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

Caracteristici

Introduces a new method to increase value from data science and machine learning Helps you get past challenges, barriers, and bottlenecks to create value from data Guides you in choices for prioritizing analytical work, adopting ways of working, choosing technology, and organizing teams