[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117061-en":3,"doc-seo-117061-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117061,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Systems for Big Data - Effectiveness and Key Platforms","Machine learning systems for big data are presented as a practical way to process rapidly growing data volumes and convert them into actionable insights. The work focuses on the purpose, structure, and effectiveness of such systems for real-world problem solving under constraints of scale, memory capacity, and high processing speed. Hadoop is highlighted as a distributed platform for large-scale processing, and alternatives such as Apache Spark and Apache Flink are discussed for batch and streaming scenarios. Key value and challenges are considered.","UDC 519.6:004 .4  \nMACHINE LEARNING SYSTEMS FOR BIG DATA  \nLiubchenko Tetiana – student, [i.troni228@gmail.com](i.troni228@gmail.com)[ ](i.troni228@gmail.com)Gudkova N.– PhD in Philology, Associate Professor, [gudkova.nm@knutd.com.ua](gudkova.nm@knutd.com.ua)[ ](gudkova.nm@knutd.com.ua)Kyiv National University of Technologies and Design  \nMachine learning systems for big data are an important tool in today's world, where data volumes are increasing every year. These systems can process and analyze vast amounts of data using machine learning algorithms, allowing valuable insights and effective decisions to be made.  \nThe aim of this work is to describe machine learning systems that are used to process big data and evaluate their effectiveness in solving real-world problems.  \nOne of the main problems when working with big data is its size. When processing big data, not only a sufficiently large amount of memory is required, but also the speed of data processing must be high to ensure fast decision-making. To solve this problem, various machine learning algorithms are used to efficiently process and analyze large amounts of data.  \nA major machine learning system for processing big data is Hadoop. Hadoop isan open platform for big data processing, which is based on machine learning algorithms and allows processing huge amounts of data. Hadoop uses a distributed architecture, which allows it to run on clusters of servers and provide high-speed data processing.  \nIn addition, there are other machine learning systems that can also be used to process big data, such as Apache Spark and Apache Flink. Apache Spark is a highperformance data processing system that uses in-memory computing and distributed architecture. Apache Flink, on the other hand, allows you to process streaming data in real time.  \nEven in the case of big data, one of the main benefits of machine learning systems is the ability to automatically extract knowledge from the data. These systems can be used to analyze data and look for hidden patterns, allowing valuable insights and effective decisions to be made. However, when working with machine learning systems to process big data, it is important to consider some of the challenges associated with high hardware and skill requirements. Nevertheless, with the right setup and use of these systems, it is possible to draw valuable conclusionsand make effective decisions based on the analysis of large amounts of data.  \nIn conclusion, machine learning systems for big data have enormous potential and are an important component of data handling in the modern world. Advances in technology in this area and the continued growth of data volumes will further support  \nthe development of machine learning systems for big data and the creation of more effective tools for handling data.  \nR e f e r e n c e  \n1. Machine Learning: Algorithms, Real-World Applications and Research Directions- SN Computer Science. SpringerLink. URL:  \n[https://link.springer.com/article/10.1007/s42979-021-00592-x](https://link.springer.com/article/10.1007/s42979-021-00592-x) (date of access: 18.04.2023).  \n2. Audry S. Art in the age of machine learning. Mit Press, 2021. 214 p.  \n3. Machine learning on big data: Opportunities and challenges / L. Zhou et al. Neurocomputing. 2017. Vol. 237. P. 350–361.","cbCaieWoSpDbKx9y","https://ap.wps.com/l/cbCaieWoSpDbKx9y","pdf",369293,1,2,"English","en",105,"# Introduction\n## Problem of Big Data Scale and Performance\n# Major Systems for Big Data\n## Hadoop\n## Apache Spark\n## Apache Flink\n# Benefits and Challenges\n## Knowledge Extraction from Data\n# Conclusion","[{\"question\":\"What is the main goal of the work on machine learning systems for big data?\",\"answer\":\"To describe machine learning systems used to process big data and to evaluate their effectiveness for solving real-world problems.\"},{\"question\":\"Why is big data processing challenging, according to the document?\",\"answer\":\"Because large data volumes require sufficient memory and high processing speed to support fast decision-making.\"},{\"question\":\"What are the main machine learning platforms mentioned for big data?\",\"answer\":\"Hadoop is emphasized for distributed processing, while Apache Spark is presented for in-memory distributed batch processing and Apache Flink for real-time streaming processing.\"}]","Machine Learning Systems for Big Data - 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