Mlops Engineering at Scale
暫譯: 大規模 MLOps 工程實踐

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 (庫存 < 4)

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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)

避免耗費成本和時間的基礎設施任務，快速將您的機器學習模型投入生產，使用 MLOps 和預建的無伺服器工具！

在《MLOps Engineering at Scale》中，您將學習：

提取、轉換和加載數據集
使用 SQL 查詢數據集
理解 PyTorch 中的自動微分
將模型訓練管道部署為服務端點
監控和管理您的管道生命週期
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，將機器學習高效地投入生產。您將學習如何快速創建靈活且可擴展的機器學習系統，而無需耗費時間在繁瑣的操作任務上或承擔昂貴的實體硬體開銷。根據計算計程車費用的實際案例，您將為 PyTorch 模型工程設計一個 MLOps 管道，利用 AWS 的無伺服器功能。

購買印刷書籍可獲得 Manning Publications 提供的免費 PDF、Kindle 和 ePub 格式電子書。

關於技術
一個準備投入生產的機器學習系統包括高效的數據管道、集成監控以及根據需求進行擴展和縮減的手段。使用基於雲的服務來實現 ML 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，讓您可以專注於數據、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來實現高效的機器學習系統。這本易於跟隨的書籍逐步指導您設置無伺服器的 ML 基礎設施，即使您從未使用過雲平台。您還將探索像 PyTorch Lightning、Optuna 和 MLFlow 等工具，這些工具使您能夠輕鬆構建管道並在生產中擴展深度學習模型。

內容概覽

減少或消除 ML 基礎設施管理
學習最先進的 MLOps 工具，如 PyTorch Lightning 和 MLFlow
將訓練管道部署為服務端點
監控和管理您的管道生命週期
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產業工作，專注於大數據分析和機器學習的專案，特別是在多核心、分散式系統中，如服務導向架構和雲端計算平台。在IBM工作期間，Carl 協助IBM軟體集團制定其策略，圍繞使用Docker和其他基於容器的技術進行無伺服器雲端計算，利用IBM Cloud和Amazon Web Services。在Google，Carl 向世界頂尖的機器學習專家學習，並協助管理公司在Google Cloud和TensorFlow上推動人工智慧民主化的努力。Carl 是超過20篇專業、貿易和學術期刊文章的作者；擁有六項美國專利商標局（USPTO）的專利；並且獲得IBM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)

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

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