Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks (Paperback)

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations

Key Features:

• Learn to build robust ML pipeline solutions for databricks transition
• Master commonly available features like AutoML and MLflow
• Leverage data governance and model deployment using MLflow model registry
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform.

You'll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you'll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You'll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows.

By the end of this book, you'll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.

What You Will Learn:

• Transition smoothly from DIY setups to databricks
• Master AutoML for quick ML experiment setup
• Automate model retraining and deployment
• Leverage databricks feature store for data prep
• Use MLflow for effective experiment tracking
• Gain practical insights for scalable ML solutions
• Find out how to handle model drifts in production environments

Who this book is for:

This book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

將您提供的文字翻譯成繁體中文如下：

將您的機器學習技能提升到更高的水平，通過掌握 Databricks 並構建堅固的機器學習流程解決方案，為未來的機器學習創新做好準備。

主要特點：
- 學習為 Databricks 過渡構建堅固的機器學習流程解決方案
- 掌握常用功能，如 AutoML 和 MLflow
- 利用 MLflow 模型註冊表實現數據治理和模型部署
- 購買印刷版或 Kindle 版本的書籍將包含免費的 PDF 電子書

書籍描述：
通過這本全面指南，釋放 Databricks 在端到端機器學習中的潛力，專為有經驗的數據科學家和開發人員從 DIY 或其他雲平台過渡而來的人士量身定制。在 Python 的堅實基礎上，實用的 Databricks 機器學習將成為您從開發到生產的路線圖，涵蓋使用 Databricks 平台的所有中間步驟。

您將首先瞭解機器學習應用、Databricks 平台功能和 MLflow 的概述。接下來，您將深入研究數據準備、模型選擇和訓練的基本要素，並發現 Databricks 特徵存儲的強大功能，用於預計算特徵表。您還將學習使用 Databricks AutoML 快速啟動項目，並通過 Databricks 工作流程自動重新訓練和部署。

通過閱讀本書，您將掌握 MLflow 的實驗跟踪、協作和模型可解釋性等高級用例。本書在每個步驟都提供了豐富的實例代碼。雖然主要關注通用功能，但本書將使您能夠輕松適應機器學習、Databricks 和 MLflow 的未來創新。

您將學到什麼：
- 從 DIY 環境順利過渡到 Databricks
- 掌握 AutoML，快速設置機器學習實驗
- 自動化模型重新訓練和部署
- 利用 Databricks 特徵存儲進行數據準備
- 使用 MLflow 進行有效的實驗跟踪
- 獲得可擴展機器學習解決方案的實用見解
- 了解如何處理生產環境中的模型漂移

本書適合對 Python、統計和機器學習生命周期熟練的有經驗的數據科學家、工程師和開發人員，他們希望從 DIY 雲平台過渡到 Databricks。具備入門級的 Spark 知識對於充分利用本書至關重要，然而，本書將涵蓋端到端的機器學習工作流程。如果您希望加快機器學習工作流程並部署可擴展、堅固的解決方案，本書是一個不可或缺的資源。