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

Building Machine Learning Pipelines

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Author : Hannes Hapke
language : en
Publisher: O'Reilly Media
Release Date : 2020-07-13

Building Machine Learning Pipelines written by Hannes Hapke 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 2020-07-13 with Computers categories.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Programming Pytorch For Deep Learning

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Author : Ian Pointer
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-20

Programming Pytorch For Deep Learning written by Ian Pointer 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 2019-09-20 with Computers categories.

Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

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.

Scaling Machine Learning With Spark

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Author : Adi Polak
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-03-07

Scaling Machine Learning With Spark written by Adi Polak 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 2023-03-07 with Computers categories.

Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture

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.

Reproducible Data Science With Pachyderm

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Author : Svetlana Karslioglu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-18

Reproducible Data Science With Pachyderm written by Svetlana Karslioglu 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 2022-03-18 with Computers categories.

Create scalable and reliable data pipelines easily with Pachyderm Key FeaturesLearn how to build an enterprise-level reproducible data science platform with PachydermDeploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and Microsoft Azure Kubernetes ServiceIntegrate Pachyderm with other data science tools, such as Pachyderm NotebooksBook Description Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale. You'll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you'll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You'll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you'll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks. By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis. What you will learnUnderstand the importance of reproducible data science for enterpriseExplore the basics of Pachyderm, such as commits and branchesUpload data to and from PachydermImplement common pipeline operations in PachydermCreate a real-life example of hyperparameter tuning in PachydermCombine Pachyderm with Pachyderm language clients in Python and GoWho this book is for This book is for new as well as experienced data scientists and machine learning engineers who want to build scalable infrastructures for their data science projects. Basic knowledge of Python programming and Kubernetes will be beneficial. Familiarity with Golang will be helpful.

Multi Cloud Automation With Ansible

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Author : Pankaj Sabharwal
language : en
Publisher: BPB Publications
Release Date : 2023-12-20

Multi Cloud Automation With Ansible written by Pankaj Sabharwal and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-20 with Computers categories.

One tool, endless possibilities: Multi-cloud mastery with Ansible KEY FEATURES ● Practical insights for efficient Ansible Tower use. ● Advanced use cases for network to edge computing. ● Multi-cloud infrastructure automation strategies. DESCRIPTION Across the modern IT landscape, managing infrastructure across diverse cloud platforms has become a formidable task. Ansible, a robust open-source automation tool, emerges as the ultimate weapon in your arsenal, unlocking efficiency and control over your multi-cloud environment, such as IBM, AWS, GCP, and Azure. Indulge in an in-depth venture through Ansible's fundamentals, architecture, and applications in multi-cloud environments with use cases. Gain a deep understanding of core concepts, such as playbooks, tasks, and roles, and learn to set up Ansible seamlessly across diverse operating systems and cloud providers. Master the creation of efficient playbooks to automate infrastructure provisioning, application deployment, and configuration management in multi-cloud scenarios. Dig into advanced areas like network automation, security automation, and edge computing, acquiring the skills to automate intricate tasks effortlessly. By the end of this book, you will emerge as a confident Ansible expert, capable of automating your multi-cloud operations with precision and efficiency. You will gain the skills to reduce manual effort, minimize errors, and achieve unprecedented agility in your cloud deployments. WHAT YOU WILL LEARN ● Write efficient Ansible Playbooks for automated system configurations. ● Deploy and manage cloud infrastructure across major providers seamlessly. ● Integrate Ansible with Kubernetes for container orchestration automation. ● Implement Ansible Automation Platform and Tower for enterprise scaling. ● Apply Ansible techniques to automate AI and deep learning pipelines. WHO THIS BOOK IS FOR This book is tailored for IT professionals, including system administrators, DevOps engineers, cloud architects, cloud security professionals, automation engineers, and network specialists seeking to leverage Ansible for automation. TABLE OF CONTENTS 1. Ansible in Multi-Cloud Environment 2. Ansible Setup Across OS and Cloud 3. Writing Tasks, Plays, and Playbooks 4. Infrastructure Automation Using Red Hat Ansible 5. Network Automation Using Ansible 6. App Automation Using Ansible 7. Security Automation Using Red Hat Ansible 8. Red Hat Ansible Automation for Edge Computing 9. Red Hat Ansible for Kubernetes and OpenShift Clusters 10. Using Ansible Automation Platform in Multi-Cloud 11. Red Hat Ansible for Deep Learning

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