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

Theory of Electromagnetic Well Logging provides a much-needed and complete analytical method for electromagnetic well logging technology. The book presents the physics and mathematics behind the effective measurement of rock properties using boreholes, allowing geophysicists, petrophysisists, geologists and engineers to interpret them in a more rigorous way. Starting with the fundamental concepts, the book then moves on to the more classic subject of wireline induction logging, before exploring the subject of LWD logging, concluding with new thoughts on electromagnetic telemetry. Theory of Electromagnetic Well Logging is the only book offering an in-depth discussion of the analytical and numerical techniques needed for expert use of those new logging techniques.