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Deploy Machine Learning Models to Production

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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Amazon SageMaker Developer Guide

build and train machine learning models, and then directly deploy them into a production-ready hosted environment.;

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Learn TensorFlow 2.0

sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented;

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Learn TensorFlow 2.0

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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Learn Amazon SageMaker

workflows on Amazon Web Services (AWS) Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning models;

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Keras to Kubernetes

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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Learn Amazon SageMaker

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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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

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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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

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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Machine Learning with SAP

Predictive Analysis Library (PAL) and machine learning functional and business services to train and deploy models. Finally, see machine learning;

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TensorFlow Machine Learning Cookbook

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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Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications

the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep;

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PyTorch Pocket Reference

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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Machine Learning for Oracle Database Professionals

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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Machine Learning with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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Mastering Azure Machine Learning

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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Building Intelligent Cloud Applications Develop Scalable Models Using Serverless Architectures with Azure

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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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

for classification and regression Build, evaluate, and deploy your machine learning solutions to production Who this book is forThis book;

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Learn TensorFlow Enterprise

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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Building Machine Learning Powered Applications Going from Idea to Product

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 2nd Edition

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data;

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Hands-On Machine Learning with C++

model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy;

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Caffe2 Quick Start Guide

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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Artificial Intelligence for IoT Cookbook

, NLP, and embedded machine learning Build, maintain, and deploy machine learning systems to extract key insights from IoT data Book;

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Kubeflow Operations Guide Managing Cloud and OnPremise Deployment

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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Building Machine Learning Pipelines Automating Model Life Cycles with Tensorflow

rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model;

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Kubeflow for Machine Learning From Lab to Production

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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