Flink-ML: machine learning in Apache Flink
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Abstract
The emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities. It functions as a versatile engine for large-scale processing, incorporating built-in modules for streaming, SQL, machine learning (ML), and visualization tasks.This paper introduces Flink-ML, Flink’s open-source distributed machine learning library, which has been added to the Flink ecosystem in response to the exponential growth of machine learning applications in recent years. Flink-ML addresses the increasing demand for scalable machine learning solutions by offering efficient implementations of a variety of algorithms. As the community around Flink continues to grow, so too does the number of contributors and available algorithms within Flink-ML.Flink-ML is designed to support multiple programming languages and provides a high-level API that leverages Flink’s rich ecosystem. This integration simplifies the development of end-to-end machine learning pipelines, allowing developers to efficiently build and deploy models. Overall, Flink-ML enhances the capabilities of the Flink framework, making it an ideal choice for organizations looking to harness the power of machine learning within their Big Data projects.