Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
Rivera, Stephanie, Prokaieva, Anastasia, Baker, Amanda
- 出版商: Packt Publishing
- 出版日期: 2024-05-17
- 售價: $1,890
- 貴賓價: 9.5 折 $1,796
- 語言: 英文
- 頁數: 280
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800564899
- ISBN-13: 9781800564893
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相關主題
商品描述
Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on
Key Features- Build machine learning solutions faster than peers only using documentation
- Enhance or refine your expertise with tribal knowledge and concise explanations
- Follow along with code projects provided in GitHub to accelerate your projects
- Purchase of the print or Kindle book includes a free PDF eBook
Discover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform.
You'll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You'll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources.
By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.
What you will learn- Set up a workspace for a data team planning to perform data science
- Monitor data quality and detect drift
- Use autogenerated code for ML modeling and data exploration
- Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows
- Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects
- Communicate insights through Databricks SQL dashboards and Delta Sharing
- Explore data and models through the Databricks marketplace
This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.
Table of Contents- Getting Started with This Book and Lakehouse Concepts
- Designing Databricks: Day One
- Building Out Our Bronze Layer
- Getting to Know Your Data
- Feature Engineering on Databricks
- Searching for a Signal
- Productionizing ML on Databricks
- Monitoring, Evaluating, and More
商品描述(中文翻譯)
深入了解在 Databricks 平台上自動生成代碼、部署機器學習算法以及利用各種機器學習生命周期功能，並遵循最佳實踐和可重用代碼的指導，讓您可以嘗試、修改和擴展。
主要特點：
- 只使用文檔，比同行更快地構建機器學習解決方案
- 通過部落知識和簡潔的解釋來增強或完善您的專業知識
- 通過在 GitHub 提供的代碼項目來加速您的項目
- 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：
發現為什麼 Databricks 數據智能平台是頂級機器學習解決方案的首選。《Databricks ML in Action》提供了與 Databricks 平台上執行數據科學、機器學習和生成式人工智能項目相關的雲無關、端到端的實例和實踐示例。
在學習如何在日常工作流程中實際應用 Databricks 的托管 MLflow、向量搜索、AutoML、Unity 目錄和模型服務的過程中，您將開發對這些技術的專業知識。這本 Databricks 書籍不僅提供了詳細的代碼解釋，還可以無縫地導入代碼以供實際使用。您將發現如何利用開源的 Databricks 平台來增強學習、提升技能並提高生產力，並提供輔助資源。
通過閱讀本書，您將掌握在數據科學、機器學習和生成式人工智能方面使用 Databricks 的能力，從而能夠交付出色的數據產品。
學到的內容：
- 為計劃進行數據科學的數據團隊設置工作空間
- 監控數據質量並檢測漂移
- 使用自動生成的代碼進行機器學習建模和數據探索
- 使用特徵工程客戶端、AutoML、向量搜索、Delta Live Tables、AutoLoader 和工作流程來實現機器學習的運營化
- 將開源和第三方應用程序（例如 OpenAI 的 ChatGPT）集成到您的人工智能項目中
- 通過 Databricks SQL 儀表板和 Delta Sharing 傳達見解
- 通過 Databricks 市場探索數據和模型
本書適合尋求在實施和利用 Databricks 數據智能平台和其 Lakehouse 架構創建數據產品方面的實踐專業知識的機器學習工程師、數據科學家和技術經理。
目錄：
1. 本書和 Lakehouse 概念入門
2. 設計 Databricks：第一天
3. 構建我們的 Bronze 層
4. 瞭解您的數據
5. 在 Databricks 上進行特徵工程
6. 尋找信號
7. 在 Databricks 上實現機器學習的生產化
8. 監控、評估和更多
