Kubernetes For Machine Learning Data Engineering
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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.
Kubernetes For Data Engineers Orchestrating Big Data And Ai Pipelines 2025
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Author : Author:1- KARAN SINGH ALANG, Author:1- Dr RUPESH MISHRA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Kubernetes For Data Engineers Orchestrating Big Data And Ai Pipelines 2025 written by Author:1- KARAN SINGH ALANG, Author:1- Dr RUPESH MISHRA and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE In today’s rapidly evolving world of data engineering, the need for scalable, efficient, and reliable infrastructure has never been more critical. With the advent of big data, artificial intelligence (AI), and machine learning (ML), the complexity of managing and deploying sophisticated data pipelines has grown exponentially. Enter Kubernetes, the open-source platform that has redefined how applications are deployed, scaled, and managed across a distributed environment. Kubernetes for Data Engineers: Orchestrating Big Data and AI Pipelines is written for data engineers, architects, and technologists who seek to leverage the power of Kubernetes in the realm of data processing and AI/ML workflows. This book serves as a practical guide for mastering the skills necessary to efficiently manage large-scale data workloads, while also offering insights into Kubernetes’ core features and its application to data-intensive tasks. Throughout this book, we explore how Kubernetes can help streamline the deployment, management, and scaling of big data technologies and AI/ML pipelines, enabling you to manage diverse tools like Hadoop, Spark, TensorFlow, and more, all within a Kubernetes environment. By adopting Kubernetes’ orchestration and automation capabilities, data engineers can drive performance, reduce overhead, and ensure resilience across the data processing lifecycle. In addition to covering fundamental Kubernetes concepts, we will also dive deep into the specific challenges faced by data engineers and how Kubernetes addresses them. From managing containerized services for distributed systems to automating data pipelines, this book will walk you through hands-on examples, case studies, and best practices to ensure you can effectively apply these concepts in your own projects. As data engineering becomes more intricate and interwoven with AI-driven innovations, the demand for Kubernetes skills will continue to rise. Whether you are already familiar with Kubernetes or just beginning to
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.
Mastering Big Data Engineering Aws Gcp Azure Showdown
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Author : Muthuraman Saminathan
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-02-16
Mastering Big Data Engineering Aws Gcp Azure Showdown written by Muthuraman Saminathan and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-16 with Business & Economics categories.
In the rapidly evolving field of AI, operationalizing large language models (LLMs) has become a defining challenge. The LLMOps Advantage: Navigating the Future of AI is your comprehensive guide to mastering the deployment, monitoring, and scaling of LLMs in real-world applications. This book bridges the gap between model development and production, introducing readers to the specialized domain of LLMOps—a subset of MLOps tailored to the unique demands of large language models. From building scalable pipelines and optimizing inference workflows to ensuring compliance and security, this guide covers every aspect of operationalizing LLMs. Explore deployment strategies across platforms like AWS, Azure, GCP, and Hugging Face, learn about containerization and serverless architectures, and dive into tools for monitoring and observability such as Prometheus and Grafana. Through practical frameworks and case studies, the book provides actionable insights into managing performance metrics, addressing model drift, and leveraging distributed systems for scalability. Designed for data scientists, LLM engineers, and AI practitioners, The LLMOps Advantage also delves into ethical considerations, emerging trends like multi-modal models, and best practices for integrating LLMs with existing workflows. Whether you ' re fine-tuning models for specific tasks or scaling solutions to meet enterprise needs, this book equips you with the expertise to harness the full potential of LLMs. Stay ahead in the AI revolution with The LLMOps Advantage—your essential roadmap to mastering the future of large language model operations.
Deploy Machine Learning Models To Production
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2020-12-15
Deploy Machine Learning Models To Production written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Computers 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. What You Will Learn 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 Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers
Google Cloud Platform For Data Engineering
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Author : Alasdair Gilchrist
language : en
Publisher: Alasdair Gilchrist
Release Date :
Google Cloud Platform For Data Engineering written by Alasdair Gilchrist and has been published by Alasdair Gilchrist this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable.
Official Google Cloud Certified Professional Data Engineer Study Guide
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Author : Dan Sullivan
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-10
Official Google Cloud Certified Professional Data Engineer Study Guide written by Dan Sullivan 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 2020-06-10 with Computers categories.
The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. Build and operationalize storage systems, pipelines, and compute infrastructure Understand machine learning models and learn how to select pre-built models Monitor and troubleshoot machine learning models Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.
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
Kubernetes For Ai And Data Engineering
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Author : Luca Randall
language : en
Publisher: Independently Published
Release Date : 2025-11-25
Kubernetes For Ai And Data Engineering written by Luca Randall and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-25 with Computers categories.
Kubernetes for AI and Data Engineering: Build Scalable Pipelines, Train Faster, and Deploy Production-Ready ML Systems AI workloads are growing faster than the systems meant to support them. Teams everywhere feel the pressure: bigger datasets, heavier models, tighter deadlines, and infrastructure that strains under the weight of real-world demands. What if you could run ETL pipelines, distributed training jobs, feature stores, vector search, and real-time inference on one unified platform, built for scale, speed, and reliability? Kubernetes for AI and Data Engineering shows you how to make that possibility real. This book gives you a practical, end-to-end blueprint for building high-performance AI systems on Kubernetes, covering everything from data ingestion and batch processing to LLM fine-tuning, GPU scheduling, model serving, observability, and multi-team governance. No abstraction. No fluff. Just the patterns, templates, and strategies used in modern, production-grade AI platforms. Inside, you'll learn how to: Run ETL and feature engineering pipelines using Spark, Ray, and Kubernetes Jobs. Train models faster with distributed GPU orchestration using Kubeflow, PyTorchJob, and advanced schedulers. Deploy resilient inference services with KServe, TensorRT, and low-latency batching strategies. Manage high-throughput streaming pipelines with Kafka, Flink, and scalable event-driven inference. Build retrieval-augmented generation workflows with vector databases and LLM serving. Implement security, cost control, and governance for multi-tenant AI clusters. Design a self-service developer experience with golden paths, templates, and internal portals. Every chapter delivers concrete, actionable knowledge you can apply immediately, whether you're a data scientist seeking independence, a machine learning engineer pushing toward production, or a platform/SRE/DevOps engineer responsible for scaling GPU-heavy workloads without blowing the budget. If you're ready to build AI pipelines that scale smoothly, train models efficiently, and deploy systems that keep up with real users and real constraints, this book gives you the tools and patterns to make it happen. Take the next step, equip yourself with the architecture, clarity, and confidence to build world-class AI systems on Kubernetes today.
Feature Engineering Bookcamp
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Author : Sinan Ozdemir
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-04
Feature Engineering Bookcamp written by Sinan Ozdemir 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 2022-10-04 with Computers categories.
Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more.