Continuous Machine Learning with Kubeflow PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Continuous Machine Learning with Kubeflow PDF full book. Access full book title Continuous Machine Learning with Kubeflow by Aniruddha Choudhury. Download full books in PDF and EPUB format.
Author: Aniruddha Choudhury
Publisher: BPB Publications
ISBN: 9389898501
Category : Computers
Languages : en
Pages : 289
Get Book Here
Book Description
An insightful journey to MLOps, DevOps, and Machine Learning in the real environment. KEY FEATURES ● Extensive knowledge and concept explanation of Kubernetes components with examples. ● An all-in-one knowledge guide to train and deploy ML pipelines using Docker and Kubernetes. ● Includes numerous MLOps projects with access to proven frameworks and the use of deep learning concepts. DESCRIPTION 'Continuous Machine Learning with Kubeflow' introduces you to the modern machine learning infrastructure, which includes Kubernetes and the Kubeflow architecture. This book will explain the fundamentals of deploying various AI/ML use cases with TensorFlow training and serving with Kubernetes and how Kubernetes can help with specific projects from start to finish. This book will help demonstrate how to use Kubeflow components, deploy them in GCP, and serve them in production using real-time data prediction. With Kubeflow KFserving, we'll look at serving techniques, build a computer vision-based user interface in streamlit, and then deploy it to the Google cloud platforms, Kubernetes and Heroku. Next, we also explore how to build Explainable AI for determining fairness and biasness with a What-if tool. Backed with various use-cases, we will learn how to put machine learning into production, including training and serving. After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies. WHAT YOU WILL LEARN ● Get comfortable with the architecture and the orchestration of Kubernetes. ● Learn to containerize and deploy from scratch using Docker and Google Cloud Platform. ● Practice how to develop the Kubeflow Orchestrator pipeline for a TensorFlow model. ● Create AWS SageMaker pipelines, right from training to deployment in production. ● Build the TensorFlow Extended (TFX) pipeline for an NLP application using Tensorboard and TFMA. WHO THIS BOOK IS FOR This book is for MLOps, DevOps, Machine Learning Engineers, and Data Scientists who want to continuously deploy machine learning pipelines and manage them at scale using Kubernetes. The readers should have a strong background in machine learning and some knowledge of Kubernetes is required. TABLE OF CONTENTS 1. Introduction to Kubeflow & Kubernetes Cloud Architecture 2. Developing Kubeflow Pipeline in GCP 3. Designing Computer Vision Model in Kubeflow 4. Building TFX Pipeline 5. ML Model Explainability & Interpretability 6. Building Weights & Biases Pipeline Development 7. Applied ML with AWS Sagemaker 8. Web App Development with Streamlit & Heroku
Author: Aniruddha Choudhury
Publisher: BPB Publications
ISBN: 9389898501
Category : Computers
Languages : en
Pages : 289
Get Book Here
Book Description
An insightful journey to MLOps, DevOps, and Machine Learning in the real environment. KEY FEATURES ● Extensive knowledge and concept explanation of Kubernetes components with examples. ● An all-in-one knowledge guide to train and deploy ML pipelines using Docker and Kubernetes. ● Includes numerous MLOps projects with access to proven frameworks and the use of deep learning concepts. DESCRIPTION 'Continuous Machine Learning with Kubeflow' introduces you to the modern machine learning infrastructure, which includes Kubernetes and the Kubeflow architecture. This book will explain the fundamentals of deploying various AI/ML use cases with TensorFlow training and serving with Kubernetes and how Kubernetes can help with specific projects from start to finish. This book will help demonstrate how to use Kubeflow components, deploy them in GCP, and serve them in production using real-time data prediction. With Kubeflow KFserving, we'll look at serving techniques, build a computer vision-based user interface in streamlit, and then deploy it to the Google cloud platforms, Kubernetes and Heroku. Next, we also explore how to build Explainable AI for determining fairness and biasness with a What-if tool. Backed with various use-cases, we will learn how to put machine learning into production, including training and serving. After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies. WHAT YOU WILL LEARN ● Get comfortable with the architecture and the orchestration of Kubernetes. ● Learn to containerize and deploy from scratch using Docker and Google Cloud Platform. ● Practice how to develop the Kubeflow Orchestrator pipeline for a TensorFlow model. ● Create AWS SageMaker pipelines, right from training to deployment in production. ● Build the TensorFlow Extended (TFX) pipeline for an NLP application using Tensorboard and TFMA. WHO THIS BOOK IS FOR This book is for MLOps, DevOps, Machine Learning Engineers, and Data Scientists who want to continuously deploy machine learning pipelines and manage them at scale using Kubernetes. The readers should have a strong background in machine learning and some knowledge of Kubernetes is required. TABLE OF CONTENTS 1. Introduction to Kubeflow & Kubernetes Cloud Architecture 2. Developing Kubeflow Pipeline in GCP 3. Designing Computer Vision Model in Kubeflow 4. Building TFX Pipeline 5. ML Model Explainability & Interpretability 6. Building Weights & Biases Pipeline Development 7. Applied ML with AWS Sagemaker 8. Web App Development with Streamlit & Heroku
Author: Trevor Grant
Publisher: "O'Reilly Media, Inc."
ISBN: 1492050075
Category : Computers
Languages : en
Pages : 264
Get Book Here
Book Description
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
Author: Josh Patterson
Publisher: "O'Reilly Media, Inc."
ISBN: 1492053228
Category : Computers
Languages : en
Pages : 331
Get Book Here
Book Description
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
Author: Chris Fregly
Publisher: "O'Reilly Media, Inc."
ISBN: 1492079367
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Author: Hannes Hapke
Publisher: "O'Reilly Media, Inc."
ISBN: 1492053147
Category : Computers
Languages : en
Pages : 358
Get Book Here
Book Description
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
Author: Mona Mona
Publisher: John Wiley & Sons
ISBN: 1119981565
Category : Computers
Languages : en
Pages : 460
Get Book Here
Book Description
Expert, guidance for the Google Cloud Machine Learning certification exam In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you’ll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer. The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments. The book also shows you how to: Frame ML problems and architect ML solutions from scratch Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards A can’t-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.
Author: Valliappa Lakshmanan
Publisher: O'Reilly Media
ISBN: 1098115759
Category : Computers
Languages : en
Pages : 408
Get Book Here
Book Description
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Author: Maurizio Morisio
Publisher: Springer Nature
ISBN: 3030641481
Category : Computers
Languages : en
Pages : 462
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 21st International Conference on Product-Focused Software Process Improvement, PROFES 2020, held in Turin, Italy, in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 19 revised full papers and 3 short papers presented were carefully reviewed and selected from 68 submissions. The papers cover a broad range of topics related to professional software development and process improvement driven by product and service quality needs. They are organized in topical sections on Agile Software Development.
Author: Hannes Hapke
Publisher: O'Reilly Media
ISBN: 1492053163
Category : Computers
Languages : en
Pages : 367
Get Book Here
Book Description
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
Author: Robert Crowe
Publisher: "O'Reilly Media, Inc."
ISBN: 1098155971
Category : Computers
Languages : en
Pages : 477
Get Book Here
Book Description
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
