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Titlebook: Data Engineering for Machine Learning Pipelines; From Python Librarie Pavan Kumar Narayanan Book 2024 Pavan Kumar Narayanan 2024 Artificial

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发表于 2025-3-21 16:24:04 | 显示全部楼层 |阅读模式
书目名称Data Engineering for Machine Learning Pipelines
副标题From Python Librarie
编辑Pavan Kumar Narayanan
视频video
概述Covers the A to Z of data engineering for ML pipelines, including data wrangling and cloud computing.Provides you with the latest technologies and methodologies, to move through the next decade of dat
图书封面Titlebook: Data Engineering for Machine Learning Pipelines; From Python Librarie Pavan Kumar Narayanan Book 2024 Pavan Kumar Narayanan 2024 Artificial
描述.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-t
出版日期Book 2024
关键词Artificial Intelligence; Machine Learning; Python; MLOps; Data Engineering; DevOps; Data Analytics; API Des
版次1
doihttps://doi.org/10.1007/979-8-8688-0602-5
isbn_softcover979-8-8688-0601-8
isbn_ebook979-8-8688-0602-5
copyrightPavan Kumar Narayanan 2024
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https://doi.org/10.1007/978-3-642-71161-9rules were defined within a limited scope. As SQL evolved within the relational systems, it provided more opportunities with respect to specifying better validation rules by writing SQL code. In the modern days of big data and machine learning, data validation has occupied greater relevance. The qua
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Operations Research Proceedings single-thread processing can be seen as a limitation. Python, by default, has a global interpreter lock (GIL) that allows only one thread to hold the interpreter at a given point in time. While this design ensures the integrity of computations submitted, it would be much more effective to use many
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Risikoaversion und optimale Konsumaufteilungta in order to make decisions. Organizations of all nature have gained significant value by leveraging machine learning models in their processes. Training a machine learning model is done using algorithms, which can be computationally expensive. As datasets grow larger, both in terms of volume and
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Johannes M. Ruhland,Klaus D. Wildeincredibly rich in functionalities. We are going to discuss Apache Kafka. Apache Kafka is a distributed and fault-tolerant streaming and messaging platform. Kafka helps build event streaming pipelines that can capture creation of new data and modification of existing data in real time and route it a
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Vergleich von Verschnittsoftwarel to deliver data services and deploy machine learning models for consumption. In this chapter, we will be discussing FastAPI. FastAPI is a Python library that primarily enables web application development and microservice development. We will look at FastAPI with the sole intention of using the lib
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