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

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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