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020 _a9798868806025
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024 7 _a10.1007/979-8-8688-0602-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
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072 7 _aCOM004000
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_2thema
100 1 _aNarayanan, Pavan Kumar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985222
245 1 0 _aData Engineering for Machine Learning Pipelines
_h[electronic resource] :
_bFrom Python Libraries to ML Pipelines and Cloud Platforms /
_cby Pavan Kumar Narayanan.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXXV, 636 p. 225 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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 _aThis 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 .
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 0 _aPython (Computer program language).
_964608
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
650 2 4 _aPython.
_947553
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868806018
776 0 8 _iPrinted edition:
_z9798868806032
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0602-5
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
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