Transactional Machine Learning With Data Streams And Automl
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Transactional Machine Learning with Data Streams and AutoML
Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.
Utilizing AI and Machine Learning in Financial Analysis
Machine learning models can imitate the cognitive process by assimilating knowledge from data and employing it to interpret and analyze information. Machine learning methods facilitate the comprehension of vast amounts of data and reveal significant patterns incorporated within it. This data is utilized to optimize financial business operations, facilitate well-informed judgements, and aid in predictive endeavors. Financial institutions utilize it to enhance pricing, minimize risks stemming from human error, mechanize repetitive duties, and comprehend client behavior. Utilizing AI and Machine Learning in Financial Analysis explores new trends in machine learning and artificial intelligence implementations in the financial sector. It examines techniques in financial analysis using intelligent technologies for improved business services. This book covers topics such as customer relations, predictive analytics, and fraud detection, and is a useful resource for computer engineers, security professionals, business owners, accountants, academicians, data scientists, and researchers.
Handbook of Research on Machine Learning Innovations and Trends
Continuous improvements in technological applications have allowed more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the latest advances and research regarding the vast range of advanced systems and applications involved in machine intelligence. Highlighting multidisciplinary studies on decision theory, intelligent search, and multi-agent systems, this publication is an ideal reference source for professionals and researchers working in the field of machine learning and its applications.