This practical book shows you how to employ machine learning models to extract information from
images. ML engineers and data scientists will learn how to solve a variety of image problems
including classification object detection autoencoders image generation counting and
captioning with proven ML techniques. This book provides a great introduction to end-to-end
deep learning: dataset creation data preprocessing model design model training evaluation
deployment and interpretability. Google engineers Valliappa Lakshmanan Martin Görner 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
or Keras. 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
