The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
暫譯: 機器學習解決方案架構師手冊:在企業環境中建立運行解決方案的機器學習平台
Ping, David
- 出版商: Packt Publishing
- 出版日期: 2022-01-21
- 售價: $3,420
- 貴賓價: 9.5 折 $3,249
- 語言: 英文
- 頁數: 440
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1801072167
- ISBN-13: 9781801072168
-
相關分類:
Machine Learning
-
相關翻譯:
機器學習平臺架構實戰 (簡中版)
買這商品的人也買了...
-
$2,460$2,337 -
$2,560$2,432 -
$480$379 -
$580$458 -
$580$458 -
$580$458 -
$680$537 -
$1,840$1,748 -
$500因特網下一站:5G與AR/VR的融合
-
$1,440AR and VR Using the Webxr API: Learn to Create Immersive Content with Webgl, Three.Js, and A-Frame (Paperback)
-
$680$537 -
$1,901Practical Deep Learning: A Python-Based Introduction
-
$2,156Parallel and High Performance Computing (Paperback)
-
$4,200$3,990 -
$2,622Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Paperback)
-
$1,010創造高清 3D 虛擬世界:Unity 引擎 HDRP 高清渲染管線實戰
-
$2,233$2,115 -
$1,665Deep Learning with PyTorch Lightning: Swiftly build high-performance Artificial Intelligence (AI) models using Python
-
$1,925$1,829 -
$4,660$4,427 -
$2,100$1,995 -
$600$468 -
$1,755Learn Three.js : Program 3D animations and visualizations for the web with JavaScript and WebGL, 4/e (Paperback)
-
$520$411 -
$1,890Enterprise AI in the Cloud: A Practical Guide to Deploying End-To-End Machine Learning and Chatgpt Solutions
商品描述
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions
Key Features:
- Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
- Build an efficient data science environment for data exploration, model building, and model training
- Learn how to implement bias detection, privacy, and explainability in ML model development
Book Description:
With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.
You'll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.
By the end of this book, you'll be able to design and build an ML platform to support common use cases and architecture patterns.
What You Will Learn:
- Apply ML methodologies to solve business problems
- Design a practical enterprise ML platform architecture
- Implement MLOps for ML workflow automation
- Build an end-to-end data management architecture using AWS
- Train large-scale ML models and optimize model inference latency
- Create a business application using an AI service and a custom ML model
- Use AWS services to detect data and model bias and explain models
Who this book is for:
This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.
商品描述(中文翻譯)
建立高度安全且可擴展的機器學習平台,以支持機器學習解決方案的快速採用
主要特點:
- 探索不同的機器學習工具和框架,以解決雲端中的大規模機器學習挑戰
- 建立高效的資料科學環境,用於資料探索、模型建立和模型訓練
- 學習如何在機器學習模型開發中實施偏見檢測、隱私和可解釋性
書籍描述:
擁有高度可擴展的機器學習(ML)平台,組織可以快速擴展機器學習產品的交付,以實現更快的商業價值,因此在各行各業對熟練的機器學習解決方案架構師的需求非常大。本書是一部實用的機器學習書籍,將帶您了解成為成功的機器學習解決方案架構師所需的設計模式、架構考量和最新技術。
您將首先了解機器學習的基本原理以及機器學習如何應用於現實世界的商業問題。在探索了一些解決不同類型問題的主要機器學習算法後,本書將幫助您掌握資料管理和使用機器學習庫,如 TensorFlow 和 PyTorch。您將學習如何使用開源技術,如 Kubernetes/Kubeflow,來建立資料科學環境和機器學習管道,然後進一步構建使用 Amazon Web Services(AWS)服務的企業機器學習架構。接著,您將涵蓋安全性和治理考量、高級機器學習工程技術,以及如何在機器學習模型開發中應用偏見檢測、可解釋性和隱私。最後,您將熟悉 AWS AI 服務及其在現實案例中的應用。
在本書結束時,您將能夠設計和建立一個支持常見用例和架構模式的機器學習平台。
您將學到的內容:
- 應用機器學習方法論解決商業問題
- 設計實用的企業機器學習平台架構
- 實施 MLOps 以自動化機器學習工作流程
- 使用 AWS 建立端到端的資料管理架構
- 訓練大規模機器學習模型並優化模型推理延遲
- 使用 AI 服務和自定義機器學習模型創建商業應用
- 使用 AWS 服務檢測資料和模型偏見並解釋模型
本書適合誰:
本書適合資料科學家、資料工程師、雲端架構師和希望成為機器學習解決方案架構師的機器學習愛好者。假設讀者具備 Python 程式語言、AWS、線性代數、機率和網路概念的基本知識。