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Practical Dataops

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Author : Harvinder Atwal
language : en
Publisher: Apress
Release Date : 2019-12-09

Practical Dataops written by Harvinder Atwal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-09 with Computers categories.

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

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Author : Noah Gift
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-09-14

Practical Mlops written by Noah Gift and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-14 with Computers categories.

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

Ibm Cloud Pak For Data

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Author : Hemanth Manda
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-11-24

Ibm Cloud Pak For Data written by Hemanth Manda and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-24 with Computers categories.

Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.

Information Systems For Intelligent Systems

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Author : Andres Iglesias
language : en
Publisher: Springer Nature
Release Date : 2025-05-30

Information Systems For Intelligent Systems written by Andres Iglesias and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Computers categories.

This book includes selected papers presented at World Conference on Information Systems for Business Management (ISBM 2024), held in Bangkok, Thailand, during September 12–13, 2024. It covers up-to-date cutting-edge research on data science, information systems, infrastructure and computational systems, engineering systems, business information systems, and smart secure systems.

Azure Data Factory By Example

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Author : Richard Swinbank
language : en
Publisher: Springer Nature
Release Date : 2024-03-22

Azure Data Factory By Example written by Richard Swinbank and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.

Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft’s cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components. This edition, updated for 2024, includes the latest developments to the Azure Data Factory service: Enhancements to existing pipeline activities such as Execute Pipeline, along with the introduction of new activities such as Script, and activities designed specifically to interact with Azure Synapse Analytics. Improvements to flow control provided by activity deactivation and the Fail activity. The introduction of reusable data flow components such as user-defined functions and flowlets. Extensions to integration runtime capabilities including Managed VNet support. The ability to trigger pipelines in response to custom events. Tools for implementing boilerplate processes such as change data capture and metadata-driven data copying. What You Will Learn Create pipelines, activities, datasets, and linked services Build reusable components using variables, parameters, and expressions Move data into and around Azure services automatically Transform data natively using ADF data flows and Power Query data wrangling Master flow-of-control and triggers for tightly orchestrated pipeline execution Publish and monitor pipelines easily and with confidence Who This Book Is For Data engineers and ETL developers taking their first steps in Azure Data Factory, SQL Server Integration Services users making the transition toward doing ETL in Microsoft’s Azure cloud, and SQL Server database administrators involved in data warehousing and ETL operations

Practical Mlops

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Author : Noah Gift
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-09-14

Agile Data Science 2 0

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Author : Russell Jurney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-07

Agile Data Science 2 0 written by Russell Jurney and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-07 with Computers categories.

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Advanced Information Systems Engineering Workshops

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Author : Jānis Grabis
language : en
Publisher: Springer Nature
Release Date : 2025-06-13

Advanced Information Systems Engineering Workshops written by Jānis Grabis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-13 with Computers categories.

This book constitutes the thoroughly refereed proceedings of the international workshops associated with the 37th International Conference on Advanced Information Systems Engineering, CAiSE 2025, which was held in Vienna, Austria, during June 16-20, 2025. The total of 24 full papers and 5 short papers included in these proceedings were carefully reviewed and selected from 59 submissions. They stem from the following workshops: - 3rd Workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE) - 3rd International Workshop on Hybrid Artificial Intelligence and Enterprise - Modelling for Intelligent Information Systems (HybridAIMS) - Joint Workshop on Blockchain for Information Systems Engineering (B4ISE) and Workshop on Information Systems and AI for Life Sciences (iSAILS) - 3rd Workshop on Modelling and Implementation of Digital Twins for Complex Systems (MIDas4CS) - Joint Process Mining with Unstructured Data workshop (PMUD) and International Workshop on Multimodal Process Mining (MMPM) - Joint Workshop on Large Language Models in Service-Oriented Architectures Design: Innovations and Applications (LLM-SOA) and Generation of Synthetic Datasets for Information Systems (GENSYN) - 1st Workshop on Compliance in the Era of Artificial Intelligence (CAI).

Data Spaces

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Author : Edward Curry
language : en
Publisher: Springer Nature
Release Date : 2022-09-08

Data Spaces written by Edward Curry and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-08 with Computers categories.

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical.

Practical Data Engineering For Cloud Migration From Legacy To Scalable Analytics 2025

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Author : Author:1- Sanchee Kaushik, Author:1- Prof. Dr. Dyuti Banerjee
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Practical Data Engineering For Cloud Migration From Legacy To Scalable Analytics 2025 written by Author:1- Sanchee Kaushik, Author:1- Prof. Dr. Dyuti Banerjee and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The exponential growth of data in today’s digital landscape has reshaped how businesses operate, forcing organizations to rethink their data strategies and technologies. As more companies embrace cloud computing, migrating legacy data systems to the cloud has become a critical step towards achieving scalability, flexibility, and agility in data management. “Practical Data Engineering for Cloud Migration: From Legacy to Scalable Analytics” serves as a comprehensive guide for professionals, data engineers, and business leaders navigating the complex but transformative journey of migrating legacy data systems to modern cloud architectures. The cloud has emerged as the cornerstone of modern data infrastructure, offering unparalleled scalability, on-demand resources, and advanced analytics capabilities. However, the transition from legacy systems to cloud-based architectures is often fraught with challenges—ranging from data compatibility issues to migration complexities, security concerns, and the need to ensure that the newly integrated systems perform optimally. This book bridges that gap by providing practical, real-world solutions for overcoming these challenges while focusing on achieving a scalable and high-performing data environment in the cloud. This book is designed to guide readers through every aspect of the cloud migration process. It starts by addressing the core principles of data engineering, data modeling, and the basics of cloud environments. From there, we delve into the specific challenges and best practices for migrating legacy data systems, transitioning databases to the cloud, optimizing data pipelines, and leveraging modern tools and platforms for scalable analytics. The chapters provide step-by-step guidance, strategies for handling large-scale data migrations, and case studies that highlight the successes and lessons learned from real-world cloud migration initiatives. Throughout this book, we emphasize the importance of ensuring that cloud migration is not just a technical task but a strategic business decision. By providing insights into how cloud migration can unlock new opportunities for data-driven innovation, this book aims to empower organizations to make informed decisions, harness the full potential of their data, and move towards more efficient and scalable cloud-native analytics solutions. Whether you are an experienced data engineer tasked with migrating legacy systems or a business leader looking to understand the strategic value of cloud data architectures, this book will provide you with the knowledge and tools necessary to execute a successful cloud migration and set your organization up for future growth. Authors

Practical Dataops

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