國立高雄大學圖資館 |

The Data Lakehouse revolutionharnessing the power of Databricks for generative AI and machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Data Lakehouse revolutionby Rajaniesh Kaushikk ; foreword by Scott Hanselman.
Reminder of title:	harnessing the power of Databricks for generative AI and machine learning /
Author:	Kaushikk, Rajaniesh.
other author:	Hanselman, Scott.
Published:	Berkeley, CA :Apress :2025.
Description:	xxiii, 451 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Microsoft Azure (Computing platform)
Online resource:	https://doi.org/10.1007/979-8-8688-1721-2
ISBN:	9798868817212$q(electronic bk.)

The Data Lakehouse revolutionharnessing the power of Databricks for generative AI and machine learning /
Kaushikk, Rajaniesh.

The Data Lakehouse revolutionharnessing the power of Databricks for generative AI and machine learning /[electronic resource] :by Rajaniesh Kaushikk ; foreword by Scott Hanselman. - Berkeley, CA :Apress :2025. - xxiii, 451 p. :ill., digital ;24 cm.

Chapter 1: Getting Started with Databricks -- Chapter 2: Introduction to Machine Learning and Data Lakehouses -- Chapter 3: Data Preparation and Management -- Chapter 4: Building Machine Learning Models -- Chapter 5: AutoML and Model Optimization -- Chapter 6: Deploying Machine Learning Models -- Chapter 7: Advanced Topics in Machine Learning -- Chapter 8: Lakehouse AI and Retrieval-Augmented Generation (RAG) -- Chapter 9: Conclusion and Next Steps.

We are racing toward a new kind of AI-faster, smarter, and more connected than ever. At the heart of it is the Data Lakehouse, and Databricks is the engine powering the transformation. Whether you're a data scientist training models, an engineer scaling pipelines, or an architect modernizing your stack, this book gives you what you need to stay ahead. Inside, you'll understand how to unlock the full potential of Machine Learning and Generative AI (GenAI) using Databricks-no fluff, just real tools, real strategies, and real results. From MLFlow and AutoML to Unity Catalog, Retrieval Augment Generation (RAG), and Vector Search, you'll get a complete blueprint for building intelligent systems that actually work in production. With step-by-step labs, industry case studies, and expert tips from someone who's lived through the entire evolution of enterprise AI, this book is your guide to mastering what's next. If you're serious regarding building AI that matters, this is where your journey begins. What You'll Learn Build full-stack ML and GenAI solutions on Databricks Train and track models with MLFlow, AutoML, and tuning strategies Secure and govern data with Unity Catalog Apply explainable, ethical AI techniques Deploy and monitor ML models in real-world pipelines Use RAG and vector search to power GenAI applications Gain confidence with hands-on labs and real enterprise use cases.

ISBN: 9798868817212$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1721-2doiSubjects--Topical Terms:

763318
Microsoft Azure (Computing platform)

LC Class. No.: TK5105.88813

Dewey Class. No.: 006.76

The Data Lakehouse revolutionharnessing the power of Databricks for generative AI and machine learning /
LDR:02939nmm a2200325 a 4500 001 690628
003 DE-He213
005 20251101120358.0
006 m d
007 cr nn 008maaau
008 260409s2025 cau s 0 eng d
020 $a 9798868817212$q(electronic bk.)
020 $a 9798868817205$q(paper)
024 7 $a 10.1007/979-8-8688-1721-2 $2 doi
035 $a 979-8-8688-1721-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.88813
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 006.76 $2 23
090 $a TK5105.88813 $b .K21 2025
100 1 $a Kaushikk, Rajaniesh. $3 1006185
245 1 4 $a The Data Lakehouse revolution $h [electronic resource] : $b harnessing the power of Databricks for generative AI and machine learning / $c by Rajaniesh Kaushikk ; foreword by Scott Hanselman.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xxiii, 451 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Getting Started with Databricks -- Chapter 2: Introduction to Machine Learning and Data Lakehouses -- Chapter 3: Data Preparation and Management -- Chapter 4: Building Machine Learning Models -- Chapter 5: AutoML and Model Optimization -- Chapter 6: Deploying Machine Learning Models -- Chapter 7: Advanced Topics in Machine Learning -- Chapter 8: Lakehouse AI and Retrieval-Augmented Generation (RAG) -- Chapter 9: Conclusion and Next Steps.
520 $a We are racing toward a new kind of AI-faster, smarter, and more connected than ever. At the heart of it is the Data Lakehouse, and Databricks is the engine powering the transformation. Whether you're a data scientist training models, an engineer scaling pipelines, or an architect modernizing your stack, this book gives you what you need to stay ahead. Inside, you'll understand how to unlock the full potential of Machine Learning and Generative AI (GenAI) using Databricks-no fluff, just real tools, real strategies, and real results. From MLFlow and AutoML to Unity Catalog, Retrieval Augment Generation (RAG), and Vector Search, you'll get a complete blueprint for building intelligent systems that actually work in production. With step-by-step labs, industry case studies, and expert tips from someone who's lived through the entire evolution of enterprise AI, this book is your guide to mastering what's next. If you're serious regarding building AI that matters, this is where your journey begins. What You'll Learn Build full-stack ML and GenAI solutions on Databricks Train and track models with MLFlow, AutoML, and tuning strategies Secure and govern data with Unity Catalog Apply explainable, ethical AI techniques Deploy and monitor ML models in real-world pipelines Use RAG and vector search to power GenAI applications Gain confidence with hands-on labs and real enterprise use cases.
650 0 $a Microsoft Azure (Computing platform) $3 763318
650 0 $a Machine learning. $3 188639
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Microsoft. $3 915087
700 1 $a Hanselman, Scott. $3 1006186
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-1721-2
950 $a Professional and Applied Computing (SpringerNature-12059)