Keras To Kubernetes
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Keras To Kubernetes
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Author : Dattaraj Rao
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-16
Keras To Kubernetes written by Dattaraj Rao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-16 with Computers categories.
Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, were seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. em style="box-sizing: border-box;"Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms. Find hands-on learning examples Learn to uses Keras and Kubernetes to deploy Machine Learning models Discover new ways to collect and manage your image and text data with Machine Learning Reuse examples as-is to deploy your models Understand the ML model development lifecycle and deployment to production If youre ready to learn about one of the most popular DL frameworks and build production applications with it, youve come to the right place!
Kubeflow Operations Guide
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Author : Josh Patterson
language : en
Publisher: O'Reilly Media
Release Date : 2020-12-04
Kubeflow Operations Guide written by Josh Patterson and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models
Mastering Tensorflow 1 X
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Author : Armando Fandango
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Mastering Tensorflow 1 X written by Armando Fandango and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-22 with Computers categories.
Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.
The Mlflow Handbook
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-05
The Mlflow Handbook written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-05 with Computers categories.
"The MLflow Handbook: End-to-End Machine Learning Lifecycle Management" is a definitive guide that equips data scientists and IT professionals with the tools and knowledge needed to effectively manage machine learning workflows. As machine learning continues to evolve, the complexity of managing models, experiments, and deployments demands robust solutions. This book provides a clear, structured approach to utilizing MLflow, an open-source platform designed to simplify and enhance every aspect of the machine learning lifecycle. Through detailed chapters, readers are introduced to setting up MLflow environments, tracking experiments, managing models, and deploying them in production. The book delves into advanced customization features, ensuring that users can tailor MLflow to meet their specific needs. Case studies across diverse industries—ranging from healthcare to retail—illustrate practical applications and underscore MLflow’s flexibility and impact. Whether a newcomer to machine learning or an experienced professional, this handbook serves as an invaluable resource to mastering MLflow and advancing machine learning capabilities efficiently and effectively.
Machine Learning Bookcamp
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Author : Alexey Grigorev
language : en
Publisher: Simon and Schuster
Release Date : 2021-11-23
Machine Learning Bookcamp written by Alexey Grigorev and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-23 with Computers categories.
Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! Machine learning bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you'll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You'll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills!
Building Intelligent Cloud Applications
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Author : John Biggs
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-10
Building Intelligent Cloud Applications written by John Biggs and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
Serverless computing is radically changing the way we build and deploy applications. With cloud providers running servers and managing machine resources, companies now can focus solely on the application’s business logic and functionality. This hands-on book shows experienced programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You’ll learn step-by-step how to code machine learning into your projects using Python and pretrained models that include tools such as image recognition, speech recognition, and classification. You’ll also examine issues around deployment and continuous delivery, including scaling, security, and monitoring. This book is divided into three parts with application examples woven throughout: Cloud-based development: Learn the basics of serverless computing with machine learning, Functions-as-a-Service (FaaS), and the use of APIs Adding intelligence: Create serverless applications using Azure Functions; learn how to use prebuilt machine learning and deep learning models Deployment and continuous delivery: Get up to speed with Azure Kubernetes Service, Azure Security Center, and Azure Monitoring
Learning Ray
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Author : Max Pumperla
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-02-13
Learning Ray written by Max Pumperla and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-13 with Computers categories.
Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR
Keras To Kubernetes
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Author : 拉奥
language : zh-CN
Publisher:
Release Date : 2020
Keras To Kubernetes written by 拉奥 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
本书从实践的角度,介绍了如何使用基于Python的Keras库和TensorFlow框架开发机器学习模型和深度学习模型,以及如何使用Kubernetes将其部署到生产环境中.
Keras Deep Learning Cookbook
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Author : Rajdeep Dua
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Keras Deep Learning Cookbook written by Rajdeep Dua and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Leverage the power of deep learning and Keras to develop smarter and more efficient data models Key FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook Description Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning What you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is for Keras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
Autokeras Deep Learning Automation
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19
Autokeras Deep Learning Automation written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
"AutoKeras Deep Learning Automation" AutoKeras Deep Learning Automation offers a thorough exploration of the evolving landscape of automated machine learning (AutoML) in deep learning, with a special focus on the capabilities and inner workings of the AutoKeras framework. Beginning with the foundational motivations behind automating deep learning model design, the book guides readers through the historical advances in neural architecture search and situates AutoKeras within the broader AutoML ecosystem. It dives into the challenges inherent to deep learning automation—such as search space complexity and model evaluation—while providing a critical comparison with other frameworks, ensuring readers understand the unique value that AutoKeras brings to contemporary AI. Moving beyond theory, this book meticulously details the technical architecture of AutoKeras, unraveling its layered system and integration with the larger TensorFlow and Keras environments. Readers are guided through comprehensive topics including automated data preprocessing, feature engineering, hyperparameter optimization, and the nuances of neural architecture search. The text also highlights advanced capabilities—such as custom model blocks, domain-specific adaptations, transfer learning, and interoperability with cloud and distributed infrastructures—empowering practitioners to efficiently automate complex workflows across a variety of domains, modalities, and deployment scenarios. In its final chapters, the book addresses the demands of production-grade deployment and scalability, detailing best practices for model export, continuous monitoring, retraining, and compliance. Real-world case studies, cutting-edge research trends, and critical topics like interpretability, fairness, and responsible AI are thoroughly discussed, ensuring that readers are equipped not only with the technical skills, but also with a forward-looking perspective on the ethical and practical dimensions of automated deep learning. "AutoKeras Deep Learning Automation" stands as an indispensable guide for engineers, researchers, and decision-makers seeking to harness the power of automation in modern AI development.