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Data engineering for machine learnin...

Narayanan, Pavan Kumar.

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Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data engineering for machine learning pipelines/ by Pavan Kumar Narayanan.
Reminder of title:	from Python libraries to ML pipelines and cloud platforms /
Author:	Narayanan, Pavan Kumar.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xxv, 636 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Data Manipulation and Analytics Using Pandas -- Chapter 2: Data Manipulation Using Polars and CuDF -- Chapter 3: Introduction to Data Validation -- Chapter 4: Data Validation Using Great Expectations -- Chapter 5: Introduction to API Design Using FastAPI -- Chapter 6: Introduction to Concurrency Programming Using Task -- Chapter 7: Dask ML -- Module 5: Data Pipelines in the Cloud -- Chapter 9: Introduction to Microsoft Azure -- Chapter 10: Introduction to Google Cloud -- Chapter 11: Introduction to Streaming Data -- Chapter 12: Introduction to Workflow Management Using Airflow -- Chapter 13: Introduction to Workflow Management Using Prefect.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/979-8-8688-0602-5
ISBN:	9798868806025

Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /
Narayanan, Pavan Kumar.

Data engineering for machine learning pipelinesfrom Python libraries to ML pipelines and cloud platforms /[electronic resource] :by Pavan Kumar Narayanan. - Berkeley, CA :Apress :2024. - xxv, 636 p. :ill., digital ;24 cm.

Chapter 1: Data Manipulation and Analytics Using Pandas -- Chapter 2: Data Manipulation Using Polars and CuDF -- Chapter 3: Introduction to Data Validation -- Chapter 4: Data Validation Using Great Expectations -- Chapter 5: Introduction to API Design Using FastAPI -- Chapter 6: Introduction to Concurrency Programming Using Task -- Chapter 7: Dask ML -- Module 5: Data Pipelines in the Cloud -- Chapter 9: Introduction to Microsoft Azure -- Chapter 10: Introduction to Google Cloud -- Chapter 11: Introduction to Streaming Data -- Chapter 12: Introduction to Workflow Management Using Airflow -- Chapter 13: Introduction to Workflow Management Using Prefect.

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure.

ISBN: 9798868806025

Standard No.: 10.1007/979-8-8688-0602-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /
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300 $a xxv, 636 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Data Manipulation and Analytics Using Pandas -- Chapter 2: Data Manipulation Using Polars and CuDF -- Chapter 3: Introduction to Data Validation -- Chapter 4: Data Validation Using Great Expectations -- Chapter 5: Introduction to API Design Using FastAPI -- Chapter 6: Introduction to Concurrency Programming Using Task -- Chapter 7: Dask ML -- Module 5: Data Pipelines in the Cloud -- Chapter 9: Introduction to Microsoft Azure -- Chapter 10: Introduction to Google Cloud -- Chapter 11: Introduction to Streaming Data -- Chapter 12: Introduction to Workflow Management Using Airflow -- Chapter 13: Introduction to Workflow Management Using Prefect.
520 $a This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure.
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650 0 $a Cloud computing. $3 1016782
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