ECOTRIALBOOK.LIFE Data Engineering for Machine Learning Pipelines From Python Libraries to ML Pipelines and Cloud Platforms Pavan Kumar Narayanan PDF 2024 BOOKS

BOOKS - Data Engineering for Machine Learning Pipelines From Python Libraries to ML P...

Data Engineering for Machine Learning Pipelines From Python Libraries to ML Pipelines and Cloud Platforms - Pavan Kumar Narayanan 2024 PDF Apress BOOKS

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Data Engineering for Machine Learning Pipelines From Python Libraries to ML Pipelines and Cloud Platforms

Author: Pavan Kumar Narayanan
Year: 2024
Pages: 631
Format: PDF
File size: 33.0 MB
Language: ENG

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Book Description: The book "Data Engineering for Machine Learning Pipelines From Python Libraries to ML Pipelines and Cloud Platforms" provides a comprehensive overview of the field of data engineering, from the basics of Python libraries to the latest advancements in cloud platforms. The book covers the entire spectrum of data engineering, from data ingestion and storage to data processing and analysis, and finally to machine learning pipelines. It offers practical guidance on how to build scalable and reliable data pipelines using Python libraries such as NumPy, pandas, and scikit-learn, as well as popular cloud platforms like AWS, GCP, and Azure. The book also explores the challenges of working with large datasets and the importance of data quality, data governance, and data security. The book begins by introducing the concept of data engineering and its role in the broader field of artificial intelligence (AI) and machine learning (ML). It explains how data engineering has evolved over time, from simple data storage solutions to complex data pipelines that power modern AI/ML applications. The authors highlight the need for a personal paradigm for perceiving the technological process of developing modern knowledge, emphasizing the importance of understanding the evolution of technology and its impact on society. They argue that this understanding is crucial for survival in today's rapidly changing world. The book then delves into the details of data ingestion, explaining how data can be extracted from various sources, such as databases, APIs, and files.

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