Building Machine Learning Pipelines
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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
Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: O'Reilly Media
Release Date : 2020-09-08
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 2020-09-08 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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines with Apache Airflow and Kubeflow Pipelines Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated Design model feedback loops to increase your data sets and learn when to update your machine learning models
Building Machine Learning Powered Applications
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Author : Emmanuel Ameisen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-01-21
Building Machine Learning Powered Applications written by Emmanuel Ameisen 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 2020-01-21 with Computers categories.
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment
Building Machine Learning And Deep Learning Models On Google Cloud Platform
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Author : Ekaba Bisong
language : en
Publisher: Apress
Release Date : 2019-09-27
Building Machine Learning And Deep Learning Models On Google Cloud Platform written by Ekaba Bisong and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-27 with Computers categories.
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
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
Operationalizing Machine Learning Pipelines
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Author : Vishwajyoti Pandey
language : en
Publisher: BPB Publications
Release Date : 2022-02-22
Operationalizing Machine Learning Pipelines written by Vishwajyoti Pandey and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-22 with Antiques & Collectibles categories.
Implementing ML pipelines using MLOps KEY FEATURES ● In-depth knowledge of MLOps, including recommendations for tools and processes. ● Includes only open-source cloud-agnostic tools for demonstrating MLOps. ● Covers end-to-end examples of implementing the whole process on Google Cloud Platform. DESCRIPTION This book will provide you with an in-depth understanding of MLOps and how you can use it inside an enterprise. Each tool discussed in this book has been thoroughly examined, providing examples of how to install and use them, as well as sample data. This book will teach you about every stage of the machine learning lifecycle and how to implement them within an organisation using a machine learning framework. With GitOps, you'll learn how to automate operations and create reusable components such as feature stores for use in various contexts. You will learn to create a server-less training and deployment platform that scales automatically based on demand. You will learn about Polyaxon for machine learning model training, and KFServing, for model deployment. Additionally, you will understand how you should monitor machine learning models in production and what factors can degrade the model's performance. You can apply the knowledge gained from this book to adopt MLOps in your organisation and tailor the requirements to your specific project. As you keep an eye on the model's performance, you'll be able to train and deploy it more quickly and with greater confidence. WHAT YOU WILL LEARN ● Quick grasp of the entire machine learning lifecycle and tricks to manage all components. ● Learn to train and validate machine learning models for scalability. ● Get to know the pros of cloud computing for scaling ML operations. ● Covers aspects of ML operations, such as reproducibility and scalability, in detail. ● Get to know how to monitor machine learning models in production. ● Learn and practice automating the ML training and deployment processes. WHO THIS BOOK IS FOR This book is intended for machine learning specialists, data scientists, and data engineers who wish to improve and increase their MLOps knowledge to streamline machine learning initiatives. Readers with a working knowledge of the machine learning lifecycle would be advantageous. TABLE OF CONTENTS 1. DS/ML Projects – Initial Setup 2. ML Projects Lifecycle 3. ML Architecture – Framework and Components 4. Data Exploration and Quantifying Business Problem 5. Training & Testing ML model 6. ML model performance measurement 7. CRUD operations with different JavaScript frameworks 8. Feature Store 9. Building ML Pipeline
Data Pipelines Pocket Reference
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Author : James Densmore
language : en
Publisher: O'Reilly Media
Release Date : 2021-02-10
Data Pipelines Pocket Reference written by James Densmore 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 2021-02-10 with Computers categories.
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
Data Science On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07
Data Science On Aws written by Chris Fregly 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 2021-04-07 with Computers categories.
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
Hands On Machine Learning With C
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Author : Kirill Kolodiazhnyi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-05-15
Hands On Machine Learning With C written by Kirill Kolodiazhnyi 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 2020-05-15 with Computers categories.
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook Description C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. What you will learnExplore how to load and preprocess various data types to suitable C++ data structuresEmploy key machine learning algorithms with various C++ librariesUnderstand the grid-search approach to find the best parameters for a machine learning modelImplement an algorithm for filtering anomalies in user data using Gaussian distributionImprove collaborative filtering to deal with dynamic user preferencesUse C++ libraries and APIs to manage model structures and parametersImplement a C++ program to solve image classification tasks with LeNet architectureWho this book is for You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.
Building Machine Learning Systems With Python
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Author : Willi Richert
language : en
Publisher: Packt Publishing Ltd
Release Date : 2013-01-01
Building Machine Learning Systems With Python written by Willi Richert 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 2013-01-01 with Computers categories.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.