OPTIMIZING MACHINE LEARNING ALGORITHMS FOR REAL-TIME DATA PROCESSING

Abstract

Real-time data processing has become an inseparable part of the computing environment since it helps to serve a huge range of applications, such as financial trading systems, industrial monitoring, driverless cars, and personalized healthcare.  These systems have the foundation of a fast decision making according to the ever changing data streams.  The necessity of machine learning (ML) systems that are able to handle and respond to real-time data has been on the rise because of unprecedented data volume generation speed assisted by sensors, social media, and Internet of Things (IoT) apparatus. The traditional machine learning process is highly resource consuming and time-consuming to satisfy the real time environment needs. Hence, there is need to optimize the ML algorithms now on a real-time basis. This will involve the efforts of ensuring that the algorithms are made to be more flexible, quick and capable of delivering low-latency time responses without compromising the accuracy. One of the strategies that are significant where the models are continuously updated with the new information as online learning progresses and enables the models to readjust as the information continuously comes in online and the models do not always have to be retrained again.  There are simpler models such as pruning and quantization that reduce models to cut on computational resources and improve the processing time.  Edge computing reduces the latency and speed of reaction of time-critical applications, by relocating the computation to the data source. Also, processing distributed across several computers or servers enhances the scale and is capable of processing extensive data streams in parallel.  This is how through the combination of these techniques, machine learning systems can be better adapted to dynamic and real-time conditions and perform better in critical applications where speed, efficiency, and adaptability are the main prioritie.