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Companies are spending billions on machine learning projects, but its money wasted if the models cant be deployed effectively. In this practical guide, Hannes Hapke and Catherine …
Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, …
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life …
Advances in 3D visualization and physics-based simulation technology make it possible for game developers to create compelling, visually immersive gaming environments that were …
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image …
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with …
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 …
Learn how to solve challenging machine learning problems with TensorFlow, Googles revolutionary new software library for deep learning. If you have some background in basic linear …
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 …