Machine Learning Model Serving Patterns and Best Practices: A definitive guide to deploying, monitoring, and providing accessibility to ML models in p

Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use

Key Features

- Learn best practices about bringing your models to production
- Explore the tools available for serving ML models and the differences between them
- Understand state-of-the-art monitoring approaches for model serving implementations

Book Description

Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.

This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples.

By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques.

What you will learn

- Explore specific patterns in model serving that are crucial for every data science professional
- Understand how to serve machine learning models using different techniques
- Discover the various approaches to stateless serving
- Implement advanced techniques for batch and streaming model serving
- Get to grips with the fundamental concepts in continued model evaluation
- Serve machine learning models using a fully managed AWS Sagemaker cloud solution

Who this book is for

This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.

成為一位成功的機器學習專業人員，輕鬆將機器學習模型部署到生產環境中，並實施基於雲端的機器學習模型，以供組織廣泛使用。

主要特點：

- 學習將模型部署到生產環境的最佳實踐
- 探索可用於提供機器學習模型的工具及其之間的差異
- 了解模型提供實現的最新監控方法

書籍描述：

提供模式使數據科學和機器學習團隊能夠將其模型部署到生產環境中。大多數機器學習模型並未針對消費者部署，因此機器學習工程師需要了解如何提供機器學習模型的關鍵步驟。

本書將涵蓋整個過程，從基本概念（如有狀態和無狀態提供）到每個方法的優點和挑戰。詳細介紹批處理、實時和連續模型提供技術。後面的章節將提供鍵值預測技術和集成模式的詳細示例。還將討論有價值的相關技術，如TensorFlow提供、BentoML和RayServe，確保您對模型提供中最重要的方法和技術有良好的理解。隨後，您將涵蓋監控和性能優化等主題，以及處理模型漂移和更新版本的策略。本書將提供實用指南和最佳實踐，確保您的模型提供流程具有強大、可擴展和可靠的特性。此外，本書還將通過詳細示例探索使用基於雲端平台和服務進行模型提供的方法，使用AWS SageMaker。

通過閱讀本書，您將能夠使用最先進的技術保存和提供您的模型。

您將學到什麼：

- 探索對每位數據科學專業人員至關重要的模型提供特定模式
- 了解使用不同技術提供機器學習模型的方法
- 發現無狀態提供的各種方法
- 實施批處理和流式模型提供的高級技術
- 掌握持續模型評估的基本概念
- 使用完全托管的AWS Sagemaker雲解決方案提供機器學習模型

本書適合對將模型投入生產環境中感興趣的機器學習工程師和數據科學家。熟悉機器學習並具有使用機器學習技術的經驗，但正在尋找將模型投入生產的選項和策略的人，將會在本書中獲得很大的價值。開始閱讀前，必須具備Python編程的工作知識。

1. Introducing Model Serving
2. Introducing Model Serving Patterns
3. Stateless Model Serving
4. Continuous Model Evaluation
5. Keyed Prediction
6. Batch Model Serving Pattern
7. Online Learning Model Serving
8. Two-Phase Model Pattern
9. Pipeline Pattern Model Serving
10. Ensemble Model Serving Pattern
11. Business Logic Pattern
12. Exploring Tensorflow Serving
13. Using Ray Serve
14. Using BentoML
15. Serving ML Models using a Fully Managed Cloud Solution

1. 介紹模型服務
2. 介紹模型服務模式
3. 無狀態模型服務
4. 持續模型評估
5. 鍵值預測
6. 批次模型服務模式
7. 在線學習模型服務
8. 兩階段模型模式
9. 流程模型服務模式
10. 集成模型服務模式
11. 商業邏輯模式
12. 探索Tensorflow服務
13. 使用Ray Serve
14. 使用BentoML
15. 使用完全托管的雲端解決方案提供機器學習模型服務