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Introducing MLOps
av Mark Treveil , Adrien Lavoillotte , Nicolas Omont m.fl.
As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets …
This minibook is a comprehensive guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete …
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a …
While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an …
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to …
As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets …
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower …
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for …
Decision intelligence (DI) has been widely named as a top technology trend for several years, and the Gartner Group reports that more than a third of large organizations are …
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in sizesmall enough to run on a microcontroller. …