Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, de

When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deployin

When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deployin

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Ha

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. Th

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

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Ha

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur w

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-pu