Kubernetes For Machine Learning
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Machine Learning On Kubernetes
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Author : Faisal Masood
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-06-24
Machine Learning On Kubernetes written by Faisal Masood 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 2022-06-24 with Computers categories.
Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies Key Features Build a complete machine learning platform on Kubernetes Improve the agility and velocity of your team by adopting the self-service capabilities of the platform Reduce time-to-market by automating data pipelines and model training and deployment Book Description MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built. What you will learn Understand the different stages of a machine learning project Use open source software to build a machine learning platform on Kubernetes Implement a complete ML project using the machine learning platform presented in this book Improve on your organization's collaborative journey toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.
Deploy Machine Learning Models To Production
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Author : Pramod Singh
language : en
Publisher:
Release Date : 2021
Deploy Machine Learning Models To Production written by Pramod Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Computer programming categories.
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. You will: Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks.
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!
Kubernetes For Machine Learning
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Author : Mark J Jaynes
language : en
Publisher: Independently Published
Release Date : 2025-11-22
Kubernetes For Machine Learning written by Mark J Jaynes and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-22 with Computers categories.
Unlock the full power of AI in production with Kubernetes the backbone of modern, scalable machine learning systems. Machine learning doesn't end when a model is trained. The real challenge begins when you deploy, scale, and monitor it in the real world. Kubernetes for Machine Learning: Deploying, Scaling, and Monitoring AI Models in Production is your practical guide to building reliable, high-performance ML systems using the industry's most trusted orchestration platform. This book breaks down the complexities of Kubernetes into clear, actionable steps tailored specifically for AI engineers, data scientists, DevOps teams, and MLOps practitioners. From containerizing ML workloads to automating pipelines, optimizing GPU usage, handling model rollouts, and observing real-time performance, you'll learn how to create production environments that can handle modern AI demands with ease. Whether you're running deep learning models, large language models, or high-throughput inference services, this guide equips you with the tools and patterns needed to ship models confidently and keep them running smoothly at scale. Benefits: End-to-end MLOps workflow: Learn how to package, deploy, and manage ML models in Kubernetes clusters. Scalable infrastructure: Master autoscaling, GPU scheduling, load balancing, and resource optimization for AI workloads. Production-grade monitoring: Implement logging, tracing, model performance tracking, and drift detection with modern observability tools. Real-world patterns: Follow proven architectures for serving APIs, batch jobs, streaming pipelines, and multi-model systems. Cloud and hybrid-ready: Apply concepts across AWS, GCP, Azure, on-prem Kubernetes, or hybrid setups. Ready to take your machine learning projects from experimentation to reliable production? Get your copy now and start building scalable, automated, and monitored AI systems with Kubernetes.
Machine Learning With Kubernetes
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Author : Martin Dunagan
language : en
Publisher: Independently Published
Release Date : 2024-11-30
Machine Learning With Kubernetes written by Martin Dunagan and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-30 with Computers categories.
Are you ready to build and deploy cutting-edge machine learning applications that can scale, adapt, and handle real-world demands? "Machine Learning with Kubernetes: A Hands-On Guide to Building, Deploying, and Managing Intelligent Applications" is your comprehensive guide to harnessing the combined power of Kubernetes and machine learning. This book provides a practical, hands-on approach to building, deploying, and managing intelligent applications at scale. Inside, you'll discover how to: Master the fundamentals: Understand the core concepts of Kubernetes and the machine learning workflow. Containerize your applications: Package your models and dependencies into portable and reproducible Docker containers. Orchestrate complex pipelines: Build and manage scalable machine learning pipelines with Argo and Kubeflow. Deploy for various use cases: Expose your models as REST APIs, gRPC services, or batch prediction systems. Scale your applications: Automatically scale your deployments to handle fluctuating workloads. Optimize for performance and cost: Efficiently manage resources and leverage GPUs for acceleration. Secure your deployments: Implement robust security measures to protect your valuable data and models. Explore advanced topics: Discover serverless machine learning, federated learning, and edge AI. This book is for you if you are: A machine learning engineer or data scientist looking to deploy models in production. A software developer interested in building scalable and reliable AI applications. A DevOps engineer responsible for managing machine learning infrastructure. Anyone who wants to learn how to leverage Kubernetes for machine learning. Gain a competitive edge by mastering the skills needed to build and deploy intelligent applications.Accelerate your machine learning workflows and bring your models to production faster.Unlock the full potential of your data and build innovative solutions that can transform your business. Don't wait! Start your journey to mastering machine learning with Kubernetes today!
Kubeflow For Machine Learning
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Author : Trevor Grant
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-13
Kubeflow For Machine Learning written by Trevor Grant 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 2020-10-13 with Computers categories.
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 a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Distributed Machine Learning Patterns
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Author : Yuan Tang
language : en
Publisher: Simon and Schuster
Release Date : 2024-01-30
Distributed Machine Learning Patterns written by Yuan Tang 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 2024-01-30 with Computers categories.
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
Kubeflow In Action
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Author : Juana Nakfour
language : en
Publisher: Manning
Release Date : 2022-03-29
Kubeflow In Action written by Juana Nakfour and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-29 with Computers categories.
Kubeflow simplifies and automates machine learning tasks like interactive analysis, complex pipelines, and model training. Seamlessly push models to production in the containerized and distributed environment and scale your ML infrastructure from your laptop to a Kubernetes cluster. Kubeflow in Action shows you how to utilize Kubeflow to rapidly scale machine learning projects from a laptop to a distributed cluster. You’ll kick off with a rapid introduction to containers, benefit from careful guidance on Kubeflow’s installation and initial setup, and master core Kubeflow tasks like storing data, training models, and monitoring metrics. Detailed use cases help show how to construct complex pipelines, automate hyperparameter tuning, and implement network architecture search. You’ll quickly progress to a deep dive into Kubeflow’s more advanced uses, including training distributed models, deployment, A/B testing, and infrastructure monitoring to help trigger actions based on incoming data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Kubernetes For Machine Learning Data Engineering
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Author : Clifford C Sowders
language : en
Publisher: Independently Published
Release Date : 2025-11-07
Kubernetes For Machine Learning Data Engineering written by Clifford C Sowders and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-07 with Computers categories.
Kubernetes for Machine Learning & Data Engineering: Build, Scale, and Automate End-to-End ML Pipelines and Data Workflows in the Cloud How do you take a brilliant machine learning model or data pipeline from your laptop to production-where it runs reliably, scales automatically, and integrates seamlessly with your organization's systems? For many teams, that question defines the boundary between experimentation and real-world impact. Kubernetes for Machine Learning & Data Engineering bridges that gap with a modern, hands-on roadmap for building, scaling, and automating ML and data workflows in the cloud. This book delivers practical, implementation-focused guidance for engineers, data scientists, and platform architects who want to harness the power of Kubernetes for complex workloads. You'll learn how to containerize ML and data applications, orchestrate distributed training and inference, manage pipelines, monitor resources, and scale intelligently-all using the same platform trusted by the world's most demanding production systems. Every chapter translates advanced Kubernetes concepts into concrete, reproducible workflows. You'll move from deploying your first ML job to managing multi-cluster architectures, integrating Airflow, Kubeflow, and MLflow, and optimizing GPU/TPU utilization for maximum performance and cost efficiency. Real examples, working manifests, and complete end-to-end architectures guide you at each step, helping you build systems that are not only functional-but future-proof. By the end of this book, you'll be able to: Build and containerize reproducible data pipelines and ML training workflows Automate feature processing, model training, and inference using Kubernetes Jobs, CronJobs, and Operators Implement distributed frameworks like Spark, Dask, and Ray on Kubernetes for large-scale data processing Deploy and autoscale ML services using KServe, Seldon Core, and serverless architectures Manage infrastructure as code with Helm, Kustomize, and Terraform Monitor, troubleshoot, and optimize performance and costs across clusters Design hybrid and multi-cloud architectures for portable, resilient workloads Whether you're modernizing legacy systems or designing your organization's next-generation ML platform, this book will help you turn Kubernetes from an infrastructure tool into a strategic advantage.
Kubeflow For Machine Learning
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Author : L. Trevor Grant
language : en
Publisher:
Release Date : 2021
Kubeflow For Machine Learning written by L. Trevor Grant and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
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 a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production.