Valliappa Lakshmanan Bücher

"Derive insights from petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery whose storage system lets you consolidate data from across your enterprise, and whose query engine enables you to condust interactive analysis and machine learning on large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from data in one convenient framework. Valliappa Lakshmanan and Jordan Tigani provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you're not familiar with or prefer to focus on specific tasks, this thorough guide is indispensable."--

Focusing on the growing need for algorithms to handle vast amounts of remotely sensed imagery, this text offers an in-depth exploration of digital processing and data mining techniques for gridded spatial data. It serves as a comprehensive resource for understanding the methodologies involved in effectively analyzing geospatial datasets.

By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. Google engineers Valliappa Lakshmanan, Martin Garner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow/Keras. This book also covers best practices to improve the operationalization of the models using end-to-end ML pipelines. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models