1.

Record Nr.

UNINA990002552080403321

Autore

Pervozvanskii, A.A.

Titolo

Random Processes in Nonlinear Control Systems / A.A. Pervozvanskii

Pubbl/distr/stampa

New York : Academic Press, 1965

Descrizione fisica

xv, 341 p. ; 24 cm

Collana

Mathematics in science and engineering ; 15

Disciplina

510

629

Locazione

MAS

Collocazione

MXVI-C-19

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9911039315103321

Autore

Kaushikk Rajaniesh

Titolo

The Data Lakehouse Revolution : Harnessing the Power of Databricks for Generative AI and Machine Learning / / by Rajaniesh Kaushikk

Pubbl/distr/stampa

Berkeley, CA : , : Apress : , : Imprint : Apress, , 2025

ISBN

9798868817212

9798868817205

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (345 pages)

Collana

Professional and Applied Computing Series

Disciplina

006.7/6

Soggetti

Microsoft Azure (Computing platform)

Machine learning

Artificial intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Nota di contenuto

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.

Sommario/riassunto

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.