Mlops Engineering at Scale

Osipov, Carl

Mlops Engineering at Scale
  • 出版商: Manning
  • 出版日期: 2022-03-16
  • 定價: $1,750
  • 售價: 9.0 折 $1,575
  • 語言: 英文
  • 頁數: 344
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617297763
  • ISBN-13: 9781617297762
  • 相關分類: Amazon Web Services、DeepLearning

    • 立即出貨

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商品描述

Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!

In MLOps Engineering at Scale you will learn:

    Extracting, transforming, and loading datasets
    Querying datasets with SQL
    Understanding automatic differentiation in PyTorch
    Deploying model training pipelines as a service endpoint
    Monitoring and managing your pipeline’s life cycle
    Measuring performance improvements

MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms.

About the book
MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production.

What's inside

    Reduce or eliminate ML infrastructure management
    Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow
    Deploy training pipelines as a service endpoint
    Monitor and manage your pipeline’s life cycle
    Measure performance improvements

商品描述(中文翻譯)

避免昂貴且耗時的基礎設施任務，並使用MLOps和預建的無伺服器工具快速將您的機器學習模型投入生產！

在《MLOps Engineering at Scale》中，您將學習到：

- 提取、轉換和加載數據集
- 使用SQL查詢數據集
- 了解PyTorch中的自動微分
- 將模型訓練管道部署為服務端點
- 監控和管理管道的生命周期
- 測量性能改進

《MLOps Engineering at Scale》向您展示如何通過使用AWS和其他雲供應商的預建服務，高效地將機器學習投入生產。您將學習如何快速創建靈活且可擴展的機器學習系統，而無需費時的操作任務或昂貴的硬體成本。通過計算計程車費用的實際案例，您將使用AWS無伺服器功能為PyTorch模型設計一個MLOps管道。

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
一個可投入生產的機器學習系統包括高效的數據管道、集成監控和根據需求的擴展能力。使用基於雲的服務來實現機器學習基礎設施可以減少開發時間並降低託管成本。無伺服器MLOps消除了構建和維護自定義基礎設施的需求，因此您可以專注於數據、模型和算法。

關於本書：
《MLOps Engineering at Scale》教您如何使用AWS和其他雲供應商的預建服務實現高效的機器學習系統。這本易於理解的書籍將逐步指導您建立無伺服器的機器學習基礎設施，即使您以前從未使用過雲平台。您還將探索像PyTorch Lightning、Optuna和MLFlow這樣的工具，這些工具可以輕鬆構建管道並在生產環境中擴展您的深度學習模型。

內容包括：
- 減少或消除機器學習基礎設施管理
- 學習PyTorch Lightning和MLFlow等最先進的MLOps工具
- 將訓練管道部署為服務端點
- 監控和管理管道的生命周期
- 測量性能改進

作者簡介

Carl Osipov has been working in the information technology industry since 2001, with a focus on projects in big data analytics and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless cloud computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world's foremost experts in machine learning and helped manage the company's efforts to democratize artificial intelligence with Google Cloud and TensorFlow. Carl is an author of over 20 articles in professional, trade, and academic journals; an inventor with six patents at USPTO; and the holder of three corporate technology awards from IBM.

作者簡介(中文翻譯)

Carl Osipov自2001年以來一直在資訊科技行業工作，專注於大數據分析和機器學習項目，涉及多核心、分散式系統，如服務導向架構和雲計算平台。在IBM期間，Carl幫助IBM軟體集團塑造了使用Docker和其他基於容器的技術進行無伺服器雲計算的策略，並使用IBM Cloud和Amazon Web Services。在Google，Carl向世界頂尖的機器學習專家學習，並協助管理該公司在Google Cloud和TensorFlow上實現人工智慧民主化的努力。Carl是專業、貿易和學術期刊上超過20篇文章的作者；他在美國專利商標局擁有六項專利；並擁有IBM頒發的三項企業技術獎。

目錄大綱

PART 1 - MASTERING THE DATA SET
1 Introduction to serverless machine learning
2 Getting started with the data set
3 Exploring and preparing the data set
4 More exploratory data analysis and data preparation
PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING
5 Introducing PyTorch: Tensor basics
6 Core PyTorch: Autograd, optimizers, and utilities
7 Serverless machine learning at scale
8 Scaling out with distributed training
PART 3 - SERVERLESS MACHINE LEARNING PIPELINE
9 Feature selection
10 Adopting PyTorch Lightning
11 Hyperparameter optimization
12 Machine learning pipeline

目錄大綱(中文翻譯)

第一部分 - 掌握資料集
1. 介紹無伺服器機器學習
2. 開始使用資料集
3. 探索和準備資料集
4. 更多探索性資料分析和資料準備

第二部分 - PyTorch 無伺服器機器學習
5. 介紹 PyTorch：張量基礎
6. PyTorch 核心：自動微分、優化器和工具
7. 大規模無伺服器機器學習
8. 使用分散式訓練進行擴展

第三部分 - 無伺服器機器學習流程
9. 特徵選擇
10. 採用 PyTorch Lightning
11. 超參數優化
12. 機器學習流程

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