Executive summary
In this Machine Learning Assignment, a detailed analysis is being provided about the latest Machine Learning that is being used by various retail industries now days. Machine learning is being taken from Artificial intelligence. A detailed difference is being also put here in order to understand the basic difference between the two. The adoption process of machine learning technology is also being described here. The benefits that this technology bears is clearly described in the below section. Various industries that use machine learning have different advantages in particular field chosen by them. Like for instance if a retail company chooses ML for the marketing section, then ML will give a prediction taking into considerations the data provided and also analysing the customers behaviour.
The present Machine Learning Assignment is showcasing how the latest ML can be utilized by various retail industries. JD is one of the Australian online retail companies who deal with a wide range of merchandise. They have noticed that in the past few decades their sales have increased very efficiently with the adoption of the e-commerce. The Chief Technology Officer of JD made a point that with the use of machine learning, the sales and the customer's experience will be increased. The CTO of JD have also mentioned that many organizations have adopted this machine learning that includes algorithms which mostly captivates the customers and make them coming back on the store again. So it can be said that machine learning is one of the most modern techniques that can be used in today's retail market in order to attract more customers. This will also make the experience of the customers better and help them customized to have their own tailoring. This machine learning also helps in the work of HR department, helps in analysing the data and helps in personal scheduling. One of the advantages of having a machine learning algorithms in retail industry is that it collects data from various customers and helps the organisation to keep a check and continuously monitoring the price of the present competitive market. This also helps to analyse the fashion trends that is going around. The main objective of the modern learning implementation is to boost the sales of the retail company and improve the experience of their target audience.
Machine learning along with the relationship and difference between artificial intelligence and machine learning
Machine language is the very popular concept, which very frequently allows the machine to learn various things from different experiences and examples, and all this is being done without actually programming it. Here, no code is being written manually, only some data is being inserted to the common algorithms. As per the ideas of Meng et al (2016), it is discussed in this report of Machine Learning Assignment that Machine learning is a subgroup of artificial learning that is mainly done by making predictions based on some experience (p. 200). It works when a new input is being introduced in the generic algorithms that help in making the prediction based on the different models. After the prediction is being evaluated completely for the accuracy process and when this accuracy is being accepted, then the Machine learning algorithms is being deployed. And when the accuracy is not being accepted, then the Machine Learning algorithms is being trained again with a more developing training set of data.
Difference between AI and ML:
ML that stands for Machine learning which is described as the procurement of skill and knowledge
AI stands for Artificial Intelligence, where artificial refers to anything that is made through humans or any non living thing, and intelligence refers to the ability to obtain and apply the knowledge
The main aim of ML is to boost the accuracy, but it most of the time neglects the success
The main aim of AI discussed in this Machine Learning Assignment is to increase the success rate but neglects the accuracy portion
In this concept the machine always takes the data and then learn from the given data
AI itself works like a computer program who does all the smart things
The very goal is to learn from the given data about certain assigned work, and increase the efficiency of the machine on that particular task
The very goal is to boost the basic intelligence that will help to solve difficult problem
ML grants the system to understand different things from the input data
It helps in taking decisions
ML is mostly involved in building self learning algorithms
AI mostly develops the system to resemble the human response in any circumstances
ML mostly goes for common solution
It goes for an optimal solution
Relationship between ML and AI:
Machine Learning is being technically derived from the branch of Artificial Intelligence, but this ML is more specific than that of the AI. The very concept of Machine Learning says that a machine can be built where the data will be processed automatically and the machine will learn from it by its own (Siau & Wang, 2018, p. 50). It will not take the consent of anyone, and will not accept the interference externally, and will analyse the data given constantly without any supervision. It can be said that Artificial Intelligence is wide and under this Machine Learning is included. ML is a part of AI, which is working with all the genetic algorithms (GA) and artificial neural network (ANN).
Three industries that uses machine learning
Various industries who are working with huge data have identified the importance of Machine Learning Technology. Collecting all the insights from the input data the organisations are quite able to perform more accurately and achieve advantages from their competitors. Some of the industries that are using the Machine Learning are:
Financial Services Industry: The financial industries often include banks and other financial institutions, are being seen using the Machine Learning technology. The main purpose to use ML is to identify all the important observations within the data, and prevent this data from misleading. This also helps in identifying the different opportunities for investments and also enabling the investors to get information about the time of trade. Data excavating also helps to identify the clients who are involved with high risk profile (Jordan & Mitchell, 2015, p. 257). It also enables the institution to have a cyber surveillance to decrease the fraud prevalent in this sector.
Financial Services Industry: The financial industries often include banks and other financial institutions, are being seen using the Machine Learning technology. The main purpose to use ML is to identify all the important observations within the data, and prevent this data from misleading. This also helps in identifying the different opportunities for investments and also enabling the investors to get information about the time of trade. Data excavating also helps to identify the clients who are involved with high risk profile (Jordan & Mitchell, 2015, p. 257). It also enables the institution to have a cyber surveillance to decrease the fraud prevalent in this sector.
Automotive Industry: The automotive industries mentioned in this Machine Learning Assignment are also taking vital steps in introducing the Machine Learning Technology to improve its operations and dealing with the big analytical data of the organisation. This technology also helps the industry in their marketing strategies and improves the customer experience that includes before and after the purchase of the commodity. The predictive analytics here helps the manufacturing department to monitor closely and share all the vital data that is related to the potential automobiles. This technology enables the manufacturer to identify the sections that deals with the breakdown with the dealership and reducing the cost of customer maintenance. This Machine Learning also gives a detailed analysis of the present trends and patterns that are going around the globe. By getting this sort of analysis from the large dataset, the owner of the manufacturing organisation can upgrade their products and also improve the customer care.
Oil and Gas Industries: Oil and Gas Industries have adopted the Machine Learning and it has become an integral part in the operating system with the organisation. This technology enables gathering a large amount of information that is collected in real time and also translating the data already set for litigable observation. The Machine Learning helps the organisations to view all the important and valuable resources and helps in creating various sights of the company (Huang et al, 2015, p. 40). Machine Learning provides various benefits to the organisation like saving time, boosting efficiency, reducing the cost and improving the security system of the organisation.
Machine Learning involves some of the key skills that are required in order to work properly. Since the ML algorithms is based on making predictions, so various mathematical tools that are needed to accord some principles of probability. Another thing which is essential for ML is the tools that help in making statistics, as ML algorithms often make statistical models which are useful for the data analysis.
Adoption process of ML in JD in Machine Learning Assignment along with advantages and disadvantages
Machine Learning is one of the branches of Artificial intelligence, which helps big organisations and companies to analyse complex data in order to cope up with the present trends and patterns going around the globe. Any organisation can take advantage of Machine Learning and there is no doubt in this. All the significance of ML will totally depend on the way the organisation have applied and what kind of problem the organisation is trying to solve. The various reports presented by this technology will depend on the resources r data is being provided to its system (Siau & Wang, 2018, p. 50). JD can explore the various aspects and benefits of ML and then evaluate the entire process and then it can finally implement it. For adopting ML, JD needs to introduce some specialised machine learning roles. This role can be data operation specialist or a machine-learning engineer, as these people are expert in developing and building ML models. By implementing some specific machine, learning success metrics JD can also improve its ML application. These specific metrics includes statistical metrics and other important metrics, which measures the data provided fairly. JD also need to implement different software in order to adapt ML. the chosen organisation also need to deploy some of the machine learning models that will give a robust checklist which includes transparency within the data and its privacy too. By introducing all this methods and application, JD will be fully able to adopt the machine learning process within the organisation.
The retail sector illustrated in the study of Machine Learning Assignment is being enjoying all the benefits by adopting the Machine Learning in their respective organisation. JD can introduce the Machine Learning in the stock and inventory sector. It is very well known that to run a business successfully the key element is the ability to establish the stock and inventory administrative process. In the retail industry machine, learning is offering the chance to buy offline as well as online data in order to predict the inventory that is obtained in real time. This will help JD to breakdown all the factors that are based on the different segments that might include a single day, a month or a season of the year (Huang et al, 2015, p. 39). JD can implement this machine learning application in predicting the behaviour of the customers. Since this technology has the capability of analysing the data related to customers and can also predict the behaviour of the customer in the future. JD can use this data for improving the pricing range through examining customer's behaviour.
The advantages that ML is that it help in predicting the future. By improving the predictability, a better promotional events can be organised and also developing the inventory to safeguard the stock that is available in the store. Also it helps to identify the latest trends and patterns for this sector.
With the benefits, ML also brings along some of the disadvantages. One of the disadvantages mentioned in this Machine Learning Assignment is that it takes time to learn all the algorithms and it needs a good amount of resources in order to provide a particular report. Sometimes the predictions provided are not relevant, so all these things need to be kept in mind after adopting Machine Learning in the retail industry.
Legal, social and ethical issues related to the application of ML on the online retail platform
Ethical issues: Data theft is one of the most common issues that online retail platforms are facing. On daily basis data breaching is taking place. Selling the products online has become a must to do thing for today's retail market. In order to do so retail companies are selling their products online, this also helps the customer, as they do not have to, personally visit the store. In addition, for buying the products online, customers need to pay online. The threat of data security arrives when all the important steps are being done online. Since customers give a lot of sensitive information to the retail sector online, so the retail organisation needs to be very careful with their data. To prevent all these, choosing the right ecommerce platform is very necessary. In addition, listing the products accurately is also necessary, as the customers cannot touch the products personally; they have to see the pictures and videos before buying the products (Meng et al.. 2016, p. 1237).As discussed in this Machine Learning Assignment, the retail company needs to maintain a good quality to maintain the reputation that it established, and a single complaint made by the customer can ruin all this.
Legal issues: Information and privacy security is a very sensitive topic in Australia. Australia has some strict rules related to this privacy issue. One of the laws is the Privacy Act 1988, which is introduced by the Australian government to protect and promote the privacy of an individual and also for the organisation. Raw data are collected and are exchanged between the systems and with multiple places, stakeholders and also the partners involved in the entire process. The transparency of the data needs to be kept in mind in order to have a smooth working of the whole process.
Social issues: Most of the organisation faces problem in educating their employees as well as customers about the application of the new technology. A few years back machine learning was a bit easier but at present it algorithms are very different from the earlier version. Here the data is mostly represented hierarchically, and have layers that requires understanding. Most of the time the engineers who are being appointed, often do not know how to do the particular process. Machine learning requires many data to prepare its algorithm, so a huge amount of resources is needed in order to get an appropriate prediction.
Conclusion
Taking into account all the information provided in Machine Learning Assignment , it can be concluded that machine learning is one of the innovative techniques that retail industry uses now a days in order to improve its performance. Other industries, for instance, financial service industry uses machine learning to identify and have an understanding of the data collected and also for protecting their data. In retail industry, technologies that are being powered by machine learning insets the data, analysis the data and at the end uses the data to illustrate and improve the shopping experience of the customers. Algorithms in ML also help to identify the similarities and differences in the data of the customers to promote and shorten the various segments for a better aiming. In the retail segment machine learning can help the customers to shop online by personalizing their chosen products and also make recommendations while modifying the price, incentives and also coupons in real time. There are some key benefits that can be found, they are, it gives offer to the customer to have a personalized recommendation regarding the products of their choice. It also offers a better price and boosts the sales as well as promotion of the organisation. Machine learning also helps in making a better inventory planning by providing right predictions. It offers a fast and more efficient delivery, which is totally based on customer behaviour and data. It also helps in segmenting the customers, which is based on the customer behaviour. However, ML also has some disadvantages that includes time taking, and requires a huge amount of resources to make a prediction.
From the above analysis within the Machine Learning Assignment, three recommendations are being made taking into consideration the Machine Learning technology:
Time taking: Since it has been observed that ML requires a lot of time to analyse all the resources provide and then after analysing it gives a report about the predictions which it have made based on the resources.
Huge resources: Machine Learning needs a very amount of data or resources that it inputs in the system, and for the analysis, it carries a huge data.
Data security: The data security is very vital for ML. so the retail organisation needs to be very careful while using the huge resources that it needs for the prediction making.
All these issues need to be kept in mind by JD in order to adopt the machine learning technology, which along with the benefits carries a number of issues, which are needed to be considered.