Wendel Patrick Books

This publication contains selected extracts from interviews with artist Andy Holden undertaken between 2010-2021.0Previously existing online, central fragments of these Q&A blogs have been rearranged thematically and intercut with a new conversation between the artist and the book?s editor, Tyler Woolcott.0With virtual or electronic information famously offering an insecure and impermanent context for knowledge ? web pages disappear and are often deleted on a whim ? one motivation for this publication is to produce a lasting and stable location for Holden?s ideas, as well as his written and verbal exchanges in printed form.0Another impetus is to create a conversation or extended dialogue that operates as a critical artwork in its own right, one that has developed over an eleven-year period with multiple participants, and contains an essence of Holden?s work as it has grown and advanced.0Speaking in 2021 on his ongoing self-reflective practice, Holden states that: ?It?s urgent and perhaps more acceptable to be sincere as a regular mode of being?. This is especially evident within ?the climate of intense sincerity dominated by identity politics, moral certainty and climate uncertainty?. 0?Collected Free Labour: Blog Interviews 2010-2021? has been published to coincide with British Art Show 9, an exhibition that includes Holden?s work and travels from Aberdeen to Wolverhampton, Manchester and Plymouth during 2021 and 2022

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 different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Learn how to set up Kubeflow on a cloud provider or on an in-house cluster Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark Learn how to add custom stages such as serving and prediction Keep your model up-to-date with Kubeflow Pipelines Understand how to validate machine learning pipelines

Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you.