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Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the;
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build and train machine learning models, and then directly deploy them into a production-ready hosted environment.;
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sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented;
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predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book;
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workflows on Amazon Web Services (AWS) Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning models;
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model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will;
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enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on;
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and deploy machine learning models to the Amazon Web Services (AWS) cloud Learn all about the AWS services available for machine learning;
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classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book;
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Predictive Analysis Library (PAL) and machine learning functional and business services to train and deploy models. Finally, see machine learning;
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Skip the theory and get the most out of Tensorflow to build production-ready machine learning models Key Features Exploit the features;
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the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep;
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acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge;
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Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's;
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Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML;
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programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You'll;
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for classification and regression Build, evaluate, and deploy your machine learning solutions to production Who this book is forThis book;
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services to scale and optimize AI models in production Book DescriptionTensorFlow as a machine learning (ML) library has matured into a;
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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on;
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Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data;
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model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy;
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Leverage the distributed capabilities of Caffe2 to build models that scale easily Book DescriptionCaffe2 is a popular deep learning library used;
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, NLP, and embedded machine learning Build, maintain, and deploy machine learning systems to extract key insights from IoT data Book;
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Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves;
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rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model;
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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;
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