TECHNOLOGY TRENDS IN MACHINE LEARNING.
ABSTRACT:
The purpose of the article is to demonstrate the applicability of solutions based on machine learning (ML) for managing contemporary corporate organisations, with a focus on improving. Decision making strives supervised as well as unsupervised learning. It defines and summarises the components of machine learning, presents results from its use, particularly in business sectors, and assesses applications, case studies, and real-world examples. The research study that is discussed in the article highlights benefits and drawbacks of the offered approach.
INTRODUCTION
The process through which computers learn to recognise patterns and develop prediction algorithms using data is a subset of artificial intelligence known as machine learning (ML) [1]. Machine learning "is built on algorithms that can learn from data without relying on rules-based programming" is a crucial aspect of the technology. It is evident that "AI-based machine learning with analytical systems to generate hybrid analysis" has been combined. The area of machine learning is immature enough that it is still evolving at a rapid rate, frequently through the invention of novel formalizations of machine-learning issues driven by practical applications (Table 1).
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LATEST TRENDS IN MACHINE LEARNING
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1. No-Code Machine Learning: Since there will be no need to write code or solve problems, the majority chances of getting results. Large data science teams are no longer required because automation reduces the need for extended development times. Because of its straightforward drag and drop interface, no-code ML [2] is simpler to use.
Figure 1. Architecture of No-Code AI?Platform
2. Tiny ML: Tiny ML joins the fray in a world where IoT solutions are taking centre stage. Although there are large-scale machine learning applications, they are not widely used. To track and anticipate data, industries utilise IoT devices with TinyML algorithms.
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Figure 2. Workflow TinyML?Platform
3. Auto ML: aims to make it easier for developers to create machine learning applications. Since machine learning has proven to be more advantageous across a variety of industries, in off shelf solutions which are in high demand. Auto-ML aims to fill the gap by simple, user-friendly solution which is does not rely on the ML specialists.
Figure 3. Auto-ML Framework
4. Machine Learning Operationalization Management (MLOps): This practise entails the development of dependable and effective machine learning software solutions. This is a revolutionary method for enhancing the creation of machine learning solutions so that they are more beneficial to organisations. Make a model based on the company's goals.
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Figure 4. Machine Learning Operations
5. Full-stack deep learning: It leads to the development of?the libraries, frameworks, and educational programmes that enable programmers to swiftly adapt to changing business requirements as well as the automation of some shipping processes (like the chitra project does) (like open source full stack deep learning projects).
MACHINE LEARNING IN PRACTICE
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WHERE ML IS APPLIED???????????????????????????????????????????????????????????????POPULAR BUSINESS T
1.Traffic Predictions????????????????????????????????????????????????????????????????????????????????????1. Amazon
2. Online Transportation Networks (ola, uber, etc)???????????????????????????????????2. Pinterest
3.?Videos Surveillance?????????????????????????????????????????????????????????????????????????????????3. Facebook
4. Social Media Services??????????????????????????????????????????????????????????????????????????????4. Netflix
5. Email Spam and Malware Filtering???????????????????????????????????????????????????????5. Twitter
6. Online Customer Support???????????????????????????????????????????????????????????????????????6. Google
7. Search Engine Result Refining??????????????????????????????????????????????????????????????7. IBM
8. Product Recommendations?????????????????????????????????????????????????????????????????????8. Apple
9. Online Fraud Detection??????????????????????????????????????????????????????????????????????????9. Microsoft
CONCLUSION
Nowadays, more than ever before, modern businesses make extensive use of machine learning. They use supervised and unsupervised solutions to support the functionality of their business, particularly to enhance decision-making and day-to-day business processes. Its application has numerous advantages that might help it obtain a competitive edge in the worldwide market and increase the worth of the company. To summarise, the most significant advantages of applying ML to management of modern company organisations are improved numerous business assessments, decision optimization, and task automation, as well as greater accuracy in the process of decision-making. As the factors influencing how AI, and particularly machine learning solutions, will develop in the future.
REFERENCES:
[1] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
[2] Iyer, C. K., Hou, F., Wang, H., Wang, Y., Oh, K., Ganguli, S., & Pandey, V. (2021, November). Trinity: A No-Code AI platform for complex spatial datasets. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 33-42).
[3] Ziora, L. (2020). Machine learning solutions in the management of a contemporary business organisation. Journal of Decision Systems, 29(sup1), 344-351.