Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production

Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow

Key Features:

• Focus on deep learning models and MLflow to develop practical business AI solutions at scale
• Ship deep learning pipelines from experimentation to production with provenance tracking
• Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility

Book Description:

The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.

From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.

By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.

What You Will Learn:

• Understand MLOps and deep learning life cycle development
• Track deep learning models, code, data, parameters, and metrics
• Build, deploy, and run deep learning model pipelines anywhere
• Run hyperparameter optimization at scale to tune deep learning models
• Build production-grade multi-step deep learning inference pipelines
• Implement scalable deep learning explainability as a service
• Deploy deep learning batch and streaming inference services
• Ship practical NLP solutions from experimentation to production

Who this book is for:

This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.

使用MLflow，在大規模上實現可重複性的情況下，訓練、測試、運行、追蹤、儲存、調整和部署具有證明性的深度學習模型和流程。

主要特點：
- 專注於深度學習模型和MLflow，以在大規模上開發實用的商業AI解決方案
- 使用證明性追蹤，從實驗到生產中運行深度學習流程
- 學習使用可解釋性和可重複性訓練、運行、調整和部署深度學習流程

書籍描述：
本書首先概述了深度學習（DL）生命週期和新興的機器學習運營（MLOps）領域，清晰地介紹了深度學習的四個支柱：數據、模型、代碼和可解釋性，以及MLflow在這些領域中的作用。

從那裡開始，本書逐步引導您了解MLflow實驗的概念和使用模式，使用MLflow作為統一框架，在大規模上追蹤DL數據、代碼和流程、模型、參數和指標。您還將處理在分佈式執行環境中運行DL流程，實現可重複性和證明性追蹤，以及使用Ray Tune、Optuna和HyperBand進行超參數優化（HPO）調整DL模型。隨著進展，您將學習如何構建具有預處理和後處理步驟的多步驟DL推理流程，使用Ray Serve和AWS SageMaker部署DL推理流程，最後使用流行的Shapley Additive Explanations（SHAP）工具箱創建DL解釋服務（EaaS）。

通過閱讀本書，您將建立基礎並獲得實踐經驗，從初始離線實驗到最終部署和生產，全部在可重複性和開源框架中完成DL流程解決方案。

您將學到什麼：
- 瞭解MLOps和深度學習生命週期開發
- 追蹤深度學習模型、代碼、數據、參數和指標
- 在任何地方構建、部署和運行深度學習模型流程
- 在大規模上運行超參數優化以調整深度學習模型
- 構建生產級的多步驟深度學習推理流程
- 實現可擴展的深度學習解釋性服務
- 部署深度學習批處理和流式推理服務
- 從實驗到生產中提供實用的NLP解決方案

本書適合機器學習從業人員，包括數據科學家、數據工程師、機器學習工程師和科學家，他們希望使用MLflow構建可重複性和證明性追蹤的可擴展全生命週期深度學習流程。理解數據科學和機器學習的基礎知識對於理解本書中介紹的概念是必要的。