۱٫ Introduction

۲٫ Research Background and Motivation

۳٫ Open Source Machine Learning Frameworks and Implementation Process

۴٫ Results and Discussion

۵٫ Conclusion

References

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.

Introduction

Machine learning applications are quickly transforming the industrial landscape. Many businesses have reduced the production and operation costs using tools powered by machine learning models and algorithms. The deep learning which is a subset of machine learning has found ways in manufacturing, industrial maintenance, drug discovery, pattern imaging analytics, and software testing [1]. The deep learning a type of deep neural network consisting of layered structure as input layer, hidden layer, and output layer. Industrial Internet of Things (IIoT) is defined as a set of machines, robotics, cognitive technologies, and computers for intelligent industrial operations with the help of data analytics [2]. The Industrial Internet of Things is a part of Industry 4.0 revolution, which is concerned with automation, innovation, big data, and cyber physical systems in industries. The Industrial Internet of Things are showing positive impact in supply chain, transportation, healthcare, manufacturing, oil and gas, energy/utilities, chemical, and aviation industry. The Industrial Internet of Things has helped in controlling and monitoring manufacturing and production from remote locations [3-5]. The Industrial Internet of Things market will reach $123.89 Billion by 2021 [6]. Industrial Internet of Things captures large chunk of data, later used for predictive maintenance, time management, and cost control after machine learning models implementation. The machine learning models forms the core of logistics and supply chain solutions in terms of optimizing the product packet size, delivery vehicle selection, delivery route selection, delivery time computation. For instance DHL uses Amazon’s Kiva robotics (improve speed, accuracy) for the network management.