Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.By applying an SRE mindset to machine lear...

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale....

Find the right big data solution for your business or organizationBig data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four ...

Decades of software testing experience condensed into the most important lessons learned.The world's leading software testing experts lend you their wisdom and years of experience to help you avoid the most common mistakes in testing software. Each lesson is an assertion related to software testing, followed by an explanation or example that shows you the how, when, and why of the testing lesson. More than just tips, tricks, and pitfalls to avoid, Lessons Learned in Software Testin...

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.Author Chip Huyen, co-founder of Claypot AI, considers each ...

Observability is critical for building, changing, and understanding the software that powers complex modern systems. Teams that adopt observability are much better equipped to ship code swiftly and confidently, identify outliers and aberrant behaviors, and understand the experience of each and every user. This practical book explains the value of observable systems and shows you how to practice observability-driven development.Authors Charity Majors, Liz Fong-Jones, and George Mira...

Following the footsteps of the first edition, the second edition of Business Intelligence is a full overview of what comprises business intelligence. It is intended to provide an introduction to the concepts to uncomplicate the learning process when implementing a business intelligence program.Over a relatively long lifetime (7 years), the current edition of book has received numerous accolades from across the industry for its straightforward introduction to both business ...

OCEB 2 Certification Guide, Second Edition has been updated to cover the new version 2 of the BPMN standard and delivers expert insight into BPM from one of the developers of the OCEB Fundamental exam, offering full coverage of the fundamental exam material for both the business and technical tracks to further certification. The first study guide prepares candidates to take—and pass—the OCEB Fundamental exam, explaining and building on basic concepts, focusing on key areas, and testing kno...

Update Your Architectural Practices for New Challenges, Environments, and Stakeholder Expectations"I am continuously delighted and inspired by the work of these authors. Their first book laid the groundwork for understanding how to evolve the architecture of a software-intensive system, and this latest one builds on it in some wonderfully actionable ways."--Grady Booch, Chief Scientist for Software Engineering, IBM Research

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintena...

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to helpWhat if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its busines...

Software architecture metrics are key to the maintainability and architectural quality of a software project and they can warn you about dangerous accumulations of architectural and technical debt early in the process. In this practical book, leading hands-on software architects share case studies to introduce metrics that every software architect should know.This isn't a book about theory. It's more about practice and implementation, about what has already been tried and worked. D...