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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.
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