Open Source Machine Learning Frameworks for Industrial Internet of Things

Information and communication technology has revolutionized the industrial operations and productions. The industries irrespective of size, whether small or large, have felt the need of artificial intelligence and machine learning techniques to process the terabytes of data generated through sensors, actuators, industrial management systems, and web applications. These data have the characteristics of volume (terabyte) and variety (image, audio, video, graphics) and thus customized models and techniques are required for analysis and management. The advancement in computer hardware, processing power, storage capacity, and cloud computing have led to experimentation and implementation of machine learning models in industrial domain for resource optimization, operation management, and quality control. However, the industrial Data Analysts face the dilemma of selecting the affordable and easy to use machine learning frameworks that suite their need and expectations. The study investigates the open source machine learning frameworks, aligned with the industrial domain (processing data generated from Industrial Internet of Things), in terms of usage, programming languages, implementations, and future prospects.