The DataOps Revolution

The approach presented in this story lines up to the six IMPACT pillars of the DataOps model that Kinaesis (www.kinaesis.com) has been using through its consultants to deliver successful projects and turn around failing deliveries.

The DataOps Revolution

DataOps is a new way of delivering data and analytics that is proven to get results. It enables IT and users to collaborate in the delivery of solutions that help organisations to embrace a data-driven culture. The DataOps Revolution: Delivering the Data-Driven Enterprise is a narrative about real world issues involved in using DataOps to make data-driven decisions in modern organisations. The book is built around real delivery examples based on the author’s own experience and lays out principles and a methodology for business success using DataOps. Presenting practical design patterns and DataOps approaches, the book shows how DataOps projects are run and presents the benefits of using DataOps to implement data solutions. Best practices are introduced in this book through the telling of a story, which relates how a lead manager must find a way through complexity to turn an organisation around. This narrative vividly illustrates DataOps in action, enabling readers to incorporate best practices into everyday projects. The book tells the story of an embattled CIO who turns to a new and untested project manager charged with a wide remit to roll out DataOps techniques to an entire organisation. It illustrates a different approach to addressing the challenges in bridging the gap between IT and the business. The approach presented in this story lines up to the six IMPACT pillars of the DataOps model that Kinaesis (www.kinaesis.com) has been using through its consultants to deliver successful projects and turn around failing deliveries. The pillars help to organise thinking and structure an approach to project delivery. The pillars are broken down and translated into steps that can be applied to real-world projects that can deliver satisfaction and fulfillment to customers and project team members.

The Unicorn Project

Gene Kim and John Willis, Beyond The Phoenix Project: The Origins and Evolution of DevOps (IT Revolution, 2018). ... Christoper Bergh, Gil Benghiat, and Eran Strod, The DataOps Cookbook: Methodologies and Tools that Reduce Analytics ...

The Unicorn Project

The Phoenix Project wowed over a half-million readers. Now comes the Wall Street Journal Bestselling The Unicorn Project! “The Unicorn Project is amazing, and I loved it 100 times more than The Phoenix Project…”—FERNANDO CORNAGO, Senior Director Platform Engineering, Adidas “Gene Kim does a masterful job of showing how … the efforts of many create lasting business advantages for all.”—DR. STEVEN SPEAR, author of The High-Velocity Edge, Sr. Lecturer at MIT, and principal of HVE LLC. “The Unicorn Project is so clever, so good, so crazy enlightening!”––CORNELIA DAVIS, Vice President Of Technology at Pivotal Software, Inc., Author of Cloud Native Patterns This highly anticipated follow-up to the bestselling title The Phoenix Project takes another look at Parts Unlimited, this time from the perspective of software development. In The Unicorn Project, we follow Maxine, a senior lead developer and architect, as she is exiled to the Phoenix Project, to the horror of her friends and colleagues, as punishment for contributing to a payroll outage. She tries to survive in what feels like a heartless and uncaring bureaucracy and to work within a system where no one can get anything done without endless committees, paperwork, and approvals. One day, she is approached by a ragtag bunch of misfits who say they want to overthrow the existing order, to liberate developers, to bring joy back to technology work, and to enable the business to win in a time of digital disruption. To her surprise, she finds herself drawn ever further into this movement, eventually becoming one of the leaders of the Rebellion, which puts her in the crosshairs of some familiar and very dangerous enemies. The Age of Software is here, and another mass extinction event looms—this is a story about rebel developers and business leaders working together, racing against time to innovate, survive, and thrive in a time of unprecedented uncertainty...and opportunity. “The Unicorn Project provides insanely useful insights on how to improve your technology business.”—DOMINICA DEGRANDIS, author of Making Work Visible and Director of Digital Transformation at Tasktop ——— “My goal in writing The Unicorn Project was to explore and reveal the necessary but invisible structures required to make developers (and all engineers) productive, and reveal the devastating effects of technical debt and complexity. I hope this book can create common ground for technology and business leaders to leave the past behind, and co-create a better future together.”—Gene Kim, November 2019

Practical DataOps

Do not underestimate the magnitude of the revolution taking place. Data is now more than an IT application input and by-product. It is an incredibly valuable raw material. Data science and data analytics are now competitive requirements ...

Practical DataOps

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.

Practical MLOps

... DataOps and Data Engineering data governance, Data Governance and Cybersecurity, Data Engineering data lake, DataOps and Data Engineering, Data Engineering data science AutoML versus, MLOps Industrial Revolution as foundational ...

Practical MLOps

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

The Chief Data Officer s Playbook

This is especially true of the senior roles, such as head of DataOps, head of data governance, chief data architect. ... However, in the data revolution, the fourth industrial revolution, businesses will fall into one of three types.

The Chief Data Officer s Playbook

This fully revised and updated edition of the bestselling Chief Data Officer’s Playbook offers new insights into the role of the CDO and the data environment. Written by two of the world’s leading experts in data driven transformation, it addresses the changes that have taken place in ‘data’, in the role of the ‘CDO’, and the expectations and ambitions of organisations. Most importantly, it will place the role of the CDO into the context of a c-suite player for organisations that wish to recover quickly and with long-term stability from the current global economic downturn. New coverage includes: - the evolution of the CDO role, what those changes mean for organisations and individuals, and what the future might hold - a focus on ethics, the data revolution and all the areas that help readers take their first steps on the data journey - new conversations and experiences from an alumni of data leaders compiled over the past three years - new chapters and reflections on being a third generation CDO and on working across a broad spectrum of organisations who are all on different parts of their data journey. Written in a highly accessible and practical manner, The Chief Data Officer’s Playbook, Second Edition brings the most up-to-date guidance to CDO’s who wish to understand their position better; to those aspiring to become CDO’s; to those who might be recruiting a CDO and to recruiters to understand an organisation seeking a CDO and the CDO landscape.

Creating a Data driven Enterprise in Media

Along the way, authors Ashish Thusoo and Joydeep Sen Sarma explain how DataOps breaks down silos and connects everyone who handles data, including engineers, data scientists, analysts, and business users.

Creating a Data driven Enterprise in Media


Entrepreneurship and Big Data

The Digital Revolution Meghna Chhabra, Rohail Hassan, Amjad Shamim ... Okera is named as a “Cool Vendor” in DataOps 2020 (“Okera Named a Cool Vendor by Gartner”, 2020), is amongst “The 10 Hottest Big Data Startups of 2020” (Whiting, ...

Entrepreneurship and Big Data

The digital age has transformed business opportunities and strategies in a resolutely practical and data-driven project universe. This book is a comprehensive and analytical source on entrepreneurship and Big Data that prospective entrepreneurs must know before embarking upon an entrepreneurial journey in this present age of digital transformation. This book provides an overview of the various aspects of entrepreneurship, function, and contemporary forms. It covers a real-world understanding of how the entrepreneurial world works and the required new analytics thinking and computational skills. It also encompasses the essential elements needed when starting an entrepreneurial journey and offers inspirational case studies from key industry leaders. Ideal reading for aspiring entrepreneurs, Entrepreneurship and Big Data: The Digital Revolution is also useful to students, academicians, researchers, and practitioners.

Data Driven Business Transformation

Easy to understand, this is a no-nonsense guide on how to mobilise and execute transformation. I particularly loved their emphasis on people being at the heart of everything and our powerhouse for the future.

Data Driven Business Transformation

OPTIMIZE YOUR BUSINESS DATA FOR FIRST-CLASS RESULTS Data Driven Business Transformation illustrates how to find the secrets to fast adaptation and disruptive origination hidden in your data and how to use them to capture market share. Digitalisation – or the Digital Revolution – was the first step in an evolving process of analysis and improvement in the operations and administration of commerce. The popular author team of Caroline Carruthers and Peter Jackson, two global leaders in data transformation and education, pick up the conversation here at the next evolutionary step where data from these digital systems generates value, and really use data science to produce tangible results. Optimise the performance of your company through data-driven processes by: Following step-by-step guidance for transitioning your company in the real world to run on a data-enabled business model Mastering a versatile set of data principles powerful enough to produce transformative results at any stage of a business’s development Winning over the hearts of your employees and influencing a cultural shift to a data-enabled business Reading first-hand stories from today’s thought leaders who are shaping data transformation at their companies Enable your company’s data to lift profits with Data Driven Business Transformation.

Fail Fast Learn Faster

... 121 Data operations (DataOps), 196 capability, achievement, 27–28 methodology, 27 Data- owner network, establishment, 100 Data- Pop Alliance, 136 “Data Revolution and Economic Analysis, The” (NBER study), 9 Davenport, Thomas H., ...

Fail Fast  Learn Faster

Explore why — now more than ever — the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact – its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become "data-driven." Fail Fast, Learn Faster includes discussions of: The emergence of Big Data and why organizations must become data-driven to survive Why becoming data-driven forces companies to "think different" about their business The state of data in the corporate world today, and the principal challenges Why companies must develop a true "data culture" if they expect to change Examples of companies that are demonstrating data-driven leadership and what we can learn from them Why companies must learn to "fail fast and learn faster" to compete in the years ahead How the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future.

Smart Algorithms for Multimedia and Imaging

... 251 Data crowdsourcing, 246 Data engineering, 245, 249 Data imbalance, 256 Data operations teams (DataOps), ... 54 loss function, 41 naïve bicubic down-sampling, 55 revolution, 251 synthetic data, 247 training and inference, ...

Smart Algorithms for Multimedia and Imaging

This book presents prospective, industrially proven methods and software solutions for storing, processing, and viewing multimedia content on digital cameras, camcorders, TV, and mobile devices. Most of the algorithms described here are implemented as systems on chip firmware or as software products and have low computational complexity and memory consumption. In the four parts of the book, which contains a total of 16 chapters, the authors address solutions for the conversion of images and videos by super-resolution, depth estimation and control and mono-to-stereo (2D to 3D) conversion; display applications by video editing; the real-time detection of sport episodes; and the generation and reproduction of natural effects. The practical principles of machine learning are illustrated using technologies such as image classification as a service, mobile user profiling, and automatic view planning with dictionary-based compressed sensing in magnetic resonance imaging. The implementation of these technologies in mobile devices is discussed in relation to algorithms using a depth camera based on a colour-coded aperture, the animated graphical abstract of an image, a motion photo, and approaches and methods for iris recognition on mobile platforms. The book reflects the authors’ practical experience in the development of algorithms for industrial R&D and the commercialization of technologies. Explains digital techniques for digital cameras, camcorders, TV, mobile devices; Offers essential algorithms for the processing pipeline in multimedia devices and accompanying software tools; Features advanced topics on data processing, addressing current technology challenges.

GeoWorld

... 1969 7:00 PM Barley Dean Coti 43438.25'N 79 * 25.4 " W Crop Stage 10 satellites OOO Crop Hght 18 Plants 8 Danes Fottes Soph Soysan Row within 3 Plants acre Data OPS Locate GeoExplorer3 THE GPS MAPPING REVOLUTION DEFINED .

GeoWorld


The Unicorn Project

This highly anticipated follow-up to the bestselling title The Phoenix Project takes another look at Parts Unlimited, this time from the perspective of software development.

The Unicorn Project

This highly anticipated follow-up to the bestselling title The Phoenix Project takes another look at Parts Unlimited, this time from the perspective of software development.

Who s who of American Women 1991 1992

Salvation Army , 1979— ; mem . bd . overseers The Hoover Instn . on War , Revolution and Peace ; mem . ... County Community Coll . , Bethlehem , 1973-75 ; data ops . control Equitable Data Ctr . , Easton , 1975-77 ; mgr . sales sves .

Who s who of American Women  1991 1992


Who s who in Government

... 1965-71 , asso . dir . data ops . , 1971-72 ; dir , systems integration Nat . ... spl . com . for minimum standards for criminal justice 1964-68 ; recipient Gold medal 1968 ) , Assn . Bar City N.Y. , Sons Revolution N.Y. Unitarian .

Who s who in Government

"Biographies of the outstanding men and women in every branch of our federal, state, county and municipal governments."--Pref.

Who s who in the East

1950 ; The Scientific Revolution and World Politics , 1964 ; contbr . to anthologies and tech . papers . ... 1972-76 , bicycle coordinator , 1976-79 , research sect . head , 1979-81 , programmer analyst data ops . , 1981 – Mem .

Who s who in the East


Generatives Deep Learning

David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt.

Generatives Deep Learning

Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.