Free Essay: Technology Selection Study Recommendation Paper

Published: 2023-02-16
Free Essay: Technology Selection Study Recommendation Paper
Type of paper:  Course work
Categories:  Learning Data analysis Information technologies Cyber security
Pages: 7
Wordcount: 1660 words
14 min read

In business production and management globally, every organization seeks to establish or identify an innovative technology to enable it to run and operate its functions effectively in a more convenient way than its local and international competitors. When the company adopts a superior technology that works at a low cost of production with an increased level of production act as its competitive advantage over the existing businesses' competitors that produce or provide the same product and services respectively and thus enables retain and sustain its dominance on the market. Therefore, this recommendation paper focuses on the recommendation of machine learning technology based on its capability feature, interaction with the environment and its processes, vulnerability or risk associated with the technology, and finally the cost and the advantages related to the adoption of the machine learning technology. The machine learning technological system is currently gaining its dominance and becoming the organizational main domain and being described as an integral part of companies' performances since it is technologically centered.

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Description of the Machine Learning Technology System

This is an emerging technological innovation data toolkit that operates automatically thus making Automated machine learning most suitable technology for an organization the technological innovation system able to deal with big data storage, data processing, and data processing in more advanced organizations and it enables the company or organization to keep and manage its big data effectively in most convenient manner thus allowing it to keep track on the performance and stability of the company or organization to surpass its rivals. With this automated learning, it is anticipated that the innovation will mark and establish a new era in terms of data-driven and support of decision-making in which business enterprises will be in a position to deploy and access many files within a short period since the machine can learn by its own. Machine learning technology contains some of the unique and essential features such as pattern recognition, automation, neural network, Data mining, Artificial intelligence, algorithms, and problem-solving tool to enable it to handle more complex tasks than any other technologies (Optis, & Perr-Sauer,2019).

Interactions of machine learning with the people and the environment.

The system is to store millions of big data of information in one single unit that does not require more spaces. The technology is also characterized by the use of the paperless principle and does n the get rid of paper which is quite expensive and contributes to environmental pollution. Environmental pollution has become one of the most challenges to organizational productivity and sustainability and currently it the primary responsibility to its environment from pollution and degradation to sustain its productivity level and standards

Capabilities and Features of Automated Machine Learning Technology

Features and capabilities of the computerized Machine learning technology system is what makes its different and unique from other existing technologies that are being utilized for the process of data storage, data processing, and data processing in a more advanced organization and it enables the company or organization to the respective companies manage its critical records such as financial, human resource records and the essential elements of organizational intellectual secrecy of production processes. Machine learning technology contains some of unique and essential features such as pattern recognition, automation, neural network, Data mining, Artificial intelligence, algorithms, and problem-solving tool to enable it to handle more complex tasks than any other technologies (Carrasquilla, & Melko,2017)

The automated Machine learning technology has high capability of driving the entire operation and controlling a lot6 of processes in the firm using the robotic program technique which enables the company that has adopted the automated Machine learning technology is able to align its process effectively and facilitate or allow other methods to run effectively using the automatic system and thus enables the company or organization to generate more income through massive and production of goods and provision of service and hence making the company to gain more from the advantage of being reliable and valid and takes control of the market. The companies and organizations that use this innovative technology system save a lot of resources used to search for the files containing innovations. For instance, searching for one file may require a lot of human labor when the information is highly needed and thus cost accompanies that has not adopted the technology since it has a library of algorithms that update the files continuously to enable easy and faster retrieval of the urgently needed document.

Automated machine learning technology has credible benefits and competitive advantage over the existing technology (Abadi, et, 2016). It has several risks associated with Automated machine learning technology in the course of its operation. Most of these risks are related to cybercrime which is gaining more roots into society and advancing its powerful mechanism and becoming a more challenging deal with. Cybercrime activity is the process of accessing personal and private data. When this happens successfully, the company or organization to lose its ownership. It is therefore essential for the company that adopts the Automated machine learning technology to put in place security mechanisms to safeguard the system from cybercrime. Financial constraints and challenges are also problems when it comes to the process of acquiring the software with its continuous update of the system make it difficult for beginners are not able to sustain and keep the cost of maintaining the technology. Regardless of the challenges, an automated learning machine has credible features as shown in the diagram below to address and prevent the occurrence of cybercrime (Witten, Frank, Hall, & Pal, 2016).

The machine is the perfect technology innovation can use detect any form of financial fraud through use of the artificial intelligence-driven solution in monitoring any arising cases of cybercrimes and scams to protect the company from losing its financial documents and illegal transactions using the company's websites (Jordan, & Mitchell, 2015).

Cost and Physical and Technological Requirements.

There is a risk that is involved in machine learning since the real procedures are problematic. In the long run, the organizations have to take up costs in making these processes a success. This is a new form of technology and has not been innovated; hence, it has uncertainties, and more funds have to be invested in this form of technology (Mori,2017). The recommended automated machine learning requires technical and physical resources which needs capital to be available. Physical resource tangible assets organization needs to put in place such area network connectivity and the building where power back-up such generators will be located to enable continuously. Similarly, the technical resource is necessary are both virtual and physical support an organization needs to structure effectively and every time system update it requires new phases of professional resource such as skilled labor The technical requirements in most cases involve automated machine learning spatial services which comprise of ICT experts and IT infrastructure which enables the company to protect its system and protect it from cybercrime fraud.

The Benefit of Machine Learning to an Organization

There are various ways the organization can benefit from machine learning which include;

  • The customer permanent value forecast- The use of data from the organizations can be used to make conclusions about the business. Machine learning can help in predicting consumer behaviors, buying patterns, and also help in the transfer of the best packages to personal customers, created by browsing and obtaining histories.
  • Abolishing manual data entry- The similarity and wrong data affect most organizations in this age. The use of creative predictive algorithms and machine learning makes the system to eliminate any errors.
  • Assistance in predictive preservation- Regularly, most organizations follow avoidance and corrective acts, but, through the use of machine learning, the organizations can get ideas from the data in the factory thus preventing failures and uncertainties in business.
  • Scanning for spam- When machine learning is used frequently in the filtering of spam through the screening of various messages.
  • Adding customer satisfaction-This is achieved through the analysis of the arising calls and making conclusions from the calls about the service the customer wants. This, in turn, decreases the expenses and the time used in creating the customer relationship with the organization, (Flatworld,2019).
  • Recommendations of goods- In this case, organizations are using machine learning in looking at the customer's buying history and compact it with a vast goods inventory to obtain concealed patterns and comparable group goods together.


Automated machine learning is an ideal technology that can execute all organizational duties and protect corporate secret information and data from cybercrime activities more effectively than any other technology making it more recommended for any existing and upcoming companies (Probst, Bischl, & Boulesteix, 2018). The machine is the perfect technology innovation accompany can use detect any form of financial fraud through use of the artificial intelligence-driven solution in monitoring any arising cases of cybercrimes and scams to protect the company from losing its financial documents and illegal transactions.


Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.

Carrasquilla, J., & Melko, R. G. (2017). Machine learning phases of matter. Nature Physics, 13(5), 431.

Flatworld. (2019). Benefits of Machine Learning in Business - Flatworld Solutions. Retrieved from

Ipsos Mori (2017). Public Views on Machine Learning

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Optis, M., & Perr-Sauer, J. (2019). The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production. Renewable and Sustainable Energy Reviews, 112, 27-41.

Probst, P., Bischl, B., & Boulesteix, A. L. (2018). Tunability: Importance of hyperparameters of machine learning algorithms. arXiv preprint arXiv:1802.09596.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

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