## 1 Introduction  
The Internet of Things (IoT) was first introduced by [PERSON] in 1999 [1]. He proposed the concept to bridge physical and virtual objects through a network of sensors and computers. This allows for the direct and indirect exchange of data between devices and applications. A review of the definition of IoT from the literature is presented in Reference [2]. Amazon Web Services (AWS) defines the IoT as a managed cloud platform that allows devices to be easily interconnected to each other and for them to securely interact with cloud applications and other devices. A simple definition from our perspective is defined as follows: IoT is the way to transform raw data, which is obtainedfrom the environment of physical systems, into useful data that can be used for monitoring purposes or decision-making by means of the internet, cloud computing, and intelligent systems. The IoT has been applied in various fields such as the fields of social sciences [2], healthcare [3], food supply chain (FSC) [4], and transportation [5]. A more detailed view of IoT applications in other fields such as the fields of logistics, smart home and buildings, smart cars, energy, safety and security, governance, education, and entertainment has been reviewed quantitatively in Reference [2]. In this paper, the application of IoT is narrowed down to industrial IoT (IIoT), especially in the manufacturing engineering field. During the October 2017 AWS public sector SUMMIT in Singapore, AWS mentioned that the IoT comprise of three pillars, namely, (1) things, (2) the cloud, and (3) intelligence. Without cloud computing and intelligent systems, the IoT is merely the transference and communication of data through the internet. This paper presents a preliminary study on cloud computing and an intelligence-based machine learning method for industrial practices. The cloud computing services hype began in 2006 when Amazon first established Amazon Web Services for small-scale developers to test their applications. Cloud computing services have now dominated the software application field due to their ability to hyper-scale the size of a project. In recent years, the development of advanced mechanical systems and manufacturing technology has been on a path of continuous high demand with more specifications due to the need for better and more consistently high-quality products, more product variability, a reduced production cost, globally competitive products, and a shorter product life cycle. The advancement of data communication and storage through the IoT leads to possibilities of connecting sensors, robots, and devices. The current manufacturing scene consists of advanced automation technologies using robots and implementing an IoT architecture. This set-up requires the integration of intelligent data processing sensors and state-of-the-art machine-to-machine (M2M) technologies with existing automation technologies. Such an architecture is required to utilize IIoT or the industrial internet in the manufacturing industries. The convergence of industrial internet infrastructures with the advent of IoT technologies can be further illustrated in Figure 1.  
Figure 1: The industrial internet: the Internet of Things (IoT) enabled industry. This paper presents a brief review of IoT applications from the perspective of the manufacturing field in order to provide significant information so as to initiate the IIoT applications. The review particularly concentrates on engineering applications rather than from the computer science perspective. The study of AWS machine learning is also validated by means of the application of the post-manufacturing process, where the goal is to predict the quality of the component using AWS-featured machine learning. This method is potentially applied to enhance the conventional measurement method in aircraft manufacturing industries.