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Last year, we published the Big Book of MLOps, outlining guiding principles, design considerations, and reference architectures for Machine Learning Operations (MLOps). Since then, Databricks has added key features simplifying MLOps, and Generative AI has brought new requirements to MLOps platforms and processes. We are excited to announce a new version of the Big Book of MLOps covering these product updates and Generative AI requirements. This blog post highlights key updates in the eBook, which can be downloaded here ... View the full article
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This week on KDnuggets: Go from learning what large language models are to building and deploying LLM apps in 7 steps • Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning • And much, much more! View the full article
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Enter the realm of "Effective DevOps," a book that promises not just theoretical concepts but practical insights into navigating the tumultuous waters of organizational dysfunction. The authors, Jennifer Davis and Ryn Daniels, bring to the table strategies that are not about quick fixes but fostering a culture of collaboration and innovation. They emphasize the human factors, encouraging a shift from reactive measures to a proactive and holistic approach to challenges in the DevOps environment... View the full article
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Whether you’re a novice stepping into the DevOps world or a seasoned technologist, "The DevOps Handbook" is your reliable companion. It’s not just a book; it’s a comprehensive guide spread across meticulous chapters, each echoing the profound insights and practical wisdom of the DevOps realm. In this review, we unfold the pages of this remarkable handbook, navigating through its enlightening content. This isn’t just another review; it’s a simplified guide, a bridge connecting you to the vast universe of DevOps knowledge encapsulated in this book... View the full article
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With this post, I’d like to share a new book that got my attention. It’s a book at the intersection of business, technology, and people. This is a great read for anyone who wants to understand how organizations can evolve to maximize the business impact of new technologies and speed up their internal processes... View the full article
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Description 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. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh URL: https://www.oreilly.com/library/view/data-mesh/9781492092384/
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