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Implementing Machine Learning in Small Business

  • Vrinda Mathur
  • May 12, 2024
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As AI and big data-powered innovations continue to alter the way we live and work, many people's understanding of the term machine learning (ML) may remain hazy, despite its rising prominence and business usefulness. 


As of the expensive expenses and sophisticated knowledge required, adopting machine learning algorithms and integrating the essential underlying technologies was once out of reach for many organizations. This, however, is no longer the case. 


What is Small Business?


When it comes to defining a small business, there is no one-size-fits-all solution. It is determined by the individual factors employed in the determination.


In general, a small business is described as one that employs less than 500 people and is independently owned and run. However, according to other definitions, a small business is one with less than $5 million in annual revenue.


A tiny business is one that operates on a small scale and requires less capital investment, fewer employees, and fewer machines to operate. On a small scale Small businesses or industries are those that create goods and services on a small scale. These industries are critical to a country's economic prosperity. The owner makes a one-time investment in machinery.


Approximately 14% of small employer businesses choose to remain sole proprietors, which means they are personally liable for all business debts. In other words, if they default on a company loan or are sued by a client, their personal property (such as their home, car, and bank accounts) is at risk. 


Others elect to structure their small business as a Limited Liability Company (LLC) to protect their personal assets. This protects their personal property from seizure if they are sued or fall behind on a corporate debt. 


A tiny firm might operate from a single site, multiple locations, or even a single enormous headquarters. The number of employees, annual income, and industry all contribute to a company's size. 


Crop growers, for example, are not limited in the number of people they may employ under the SBA, but there are income constraints ($750k). Then, in order to stay "little," a retail bakery chain cannot employ more than 500 people.


Also Read | 5 Best Examples Of Machine Learning In Real Life!


Working of Machine Learning


Machine learning is a data analysis technique that automates the creation of analytical models. It is a subfield of artificial intelligence that is predicated on the premise that systems can learn from data, spot patterns, and make decisions with little or no human intervention.


Do you get automatic recommendations for films to watch next on Netflix and Amazon Prime? Or perhaps you receive selections for people you know on Facebook or LinkedIn? You may also use Siri, Alexa, and other voice assistants on your phone. That's all there is to Machine Learning! This is a technology that is growing in popularity. Machine Learning is very certainly employed in practically every technology around you!


Machine learning is both straightforward and difficult At its foundation, the strategy simply employs algorithms - essentially lists of rules - that have been tweaked and enhanced using previous data sets to generate predictions and categorize new data. A machine learning algorithm, for example, may be "trained" on a data set containing thousands of images of flowers labeled with each of their different flower types so that it can correctly identify a flower in a new photograph based on the distinguishing characteristics it learned from other pictures.  


However, in order for such algorithms to perform properly, they must often be tweaked numerous times until they gather a comprehensive list of instructions. Algorithms that have been properly taught eventually become "machine learning models," which are essentially algorithms that have been trained to perform specific tasks such as image sorting, housing price prediction, or chess moves. In other circumstances, algorithms are built on top of each other to form sophisticated networks that can do increasingly difficult, nuanced tasks such as text generation and chatbot powering using a technique known as "deep learning."


Also Read | The Different Types Of Classifiers In Machine Learning


Use Cases of Machine Learning in Small Business


The development of AI-Tables has democratized data science access. It has enabled SMEs to experiment with data sets in a cost-effective and flexible manner, without the need for expert support or well-resourced IT staff. Any database user should now be able to train and test ML models using AI-Tables as if they were regular data tables.


Here are top 5 industries where SMEs can quickly, economically, and conveniently train and test data models.


  1. Retail 


Customer-facing retail brands live or die on the customer experience they provide. ML can detect repeating patterns of consumer behavior, from when specific commodities are purchased during the day/week to how moving them influences sales. This aids in the optimisation of elements like promotional displays, just-in-time stock control, and personnel numbers.


  1. Simplify operations


AI may assist organizations by increasing efficiency and productivity in company processes. Whether it's categorizing incoming emails based on their contents or distinguishing between prospective and unpromising leads.


AI can assist with both simple and difficult challenges, allowing you and your staff to devote more time to other critical duties.


  1. Enhance your product


With insights on how people engage with products and services, AI helps businesses to continuously improve them. Simply utilize AI to monitor what users are saying and the emotion underlying their comments, and make modifications as needed. AI can assist identify product issues before they are raised by users, allowing you to remedy problems before they exist.


AI is no longer just for large corporations; small firms may now use AI to optimize their processes as well. AI solutions make it easier than ever to leverage the power of Machine Learning—let's look at how to integrate AI in your small business.


  1. Pharmaceuticals


Individual laboratories' in-silico computations have expedited the fast pace of COVID-19 vaccine research, while tiny manufacturing enterprises have profited from accurate predictions surrounding demand volumes and production capacity. If ML enables for faster and more accurate calculation of outcomes, it has the potential to save lives.


  1. Cybersecurity


Cybercriminals and IT organizations are constantly playing cat and mouse. ML can help cybersecurity teams spot emerging zero-day attacks more rapidly by learning from prior occurrences and identifying suspect patterns. This can help smaller businesses protect themselves from DDoS assaults and data theft while also protecting clients from fraud and account breaches.  


Wrapping Up


Machine Learning is employed in practically all modern technologies, and its use will only grow in the future. In fact, Machine Learning is used in a variety of industries, including smartphone technology, healthcare, and social media.


Smartphones employ personal speech assistants such as Siri, Alexa, Cortana, and others. These personal assistants are an example of ML-based speech recognition, which employs Natural Language Processing to communicate with people and construct appropriate responses. Social media also makes use of machine learning. 


Consider Facebook's 'People you may know' feature. It's mind-boggling how social media sites can anticipate who you might know in real life.


As a result of biased data, some Machine Learning Algorithms utilized in the actual world may not be objective. Companies, on the other hand, are attempting to ensure that only objective algorithms are employed. One method is to preprocess the data so that the bias is removed before training the ML algorithm on it. Another approach is to post-process the ML algorithm after it has been trained on the data such that it meets an arbitrary fairness constant that is predetermined.

Also Read | Application Of Machine Learning In A Variety Of Industries

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