February 7, 2023

Machine Learning for Small Businesses: 7 Tips to Get Started

Technology is expanding rapidly each year and challenging what is possible. One of the benefits of the expansion is the advancements it has made possible in different industries. One of the main benefactors is small businesses.

Several trends have emerged and encouraged the adoption of technology in small businesses. Some of the notable changes include the adoption of employee monitoring software, a focus on cyber security, remote onboarding, and technology-aided shopping. 

Some of the trends can be attributed to the Covid 19 Pandemic which challenged businesses to change their business models and embrace technology. One of the trends that have proved beneficial to business is the use of artificial intelligence (AI) and machine learning (ML).

The capabilities of artificial intelligence and machine learning and what can be achieved with the technologies have been popular topics over the years. We are now in a period when technologies are being adopted by various industries and they have brought numerous improvements.

ML and AI adoption in the financial sector have been immaterial in the detection of fraud and prevention of fraud. ML has also improved education through the development of different learning strategies based on each student’s learning capabilities and in the detection of plagiarism.

Your business also stands to benefit from the adoption of machine learning. Machine learning can help engage with your customers better through personalization. E-commerce platforms such as Amazon use machine learning to recommend items to their different users. 

ML can also help you with customer retention, fast decision-making, and increasing efficiency in your business. You can also leverage the data from your business transactions to get forecasts about your demands and do proper inventory management.

As a small business, you stand to benefit from incorporating ML and AI into your business. In this article, we will help you understand how you can get started and use ML for your business and improve your operations.

  1. Understanding Your Challenging Areas

The first step before you can think about ML deployment for your business is analyzing your business needs. Take time to analyze the areas in your business that need improvements, and list them according to priority.

Your business may have several areas of concern, however, it is not logical to take on all of them at once. You may notice fixing one area might take care of another problematic area. Also, you have time and budget constraints to worry about.

Therefore, it is more advisable to make incremental improvements. Once you have your list, break down the problems into parts and identify the main issue.

  1. Exploring Machine Learning Options

The next step is to explore how you can improve on the specific area of your business using machine learning. You need to understand how to apply machine learning and leverage ML deployment to solve problems.

If you are clueless about this you can seek advice from an expert or look at your competition. Decide whether you will do the entire process in-house or depend upon a third party. They can do part of the work or the entire process including data collection and ML deployment.

You can consider leveraging Saas and getting a pre-trained ML model. This can save you time and money spent in developing your solution. 

It is also important to establish where your funding will come from. You can depend on investors, loans, or your savings. Also, establish your minimum desired result to limit the number of ideas you explore. Chances are you will run out of funding if you have a large scope.

  1. Collecting Data

The next step is collecting data for your machine-learning model. You need to identify the sources of data for modeling your ML model. The complexity of your problem and the algorithm you will use for your ml model will determine the amount of data you need.

When collecting data, you should be accurate and implement quality control factors. The control factors will enable you to get clean and accurate data. The quality of data affects the quality of results. Also, the velocity of the data matters. If you can get real-time data, the better.

Get multiple sources of data. Your company website is a source of data if you leverage the different analytic tools at your disposal. Your day-to-day operations can also be a source of data. You can also get datasets online for your problem.

  1. Data Exploration 

Once you have your data, the next step is to assess the quality of the data. You need to understand the trends in the data, identify outliers, and check if it is skewed and biased.

The data needs to be balanced and free from bias. You can rely on statistical analysis methods and data visualization to help in the process. You should aim to make sure that the data has:

  • Homogeneous variance
  • No missing values
  • Normal distribution
  • No outliers
  • Independent datasets

The objective is to have balanced data for our algorithm because the quality of data affects the results.

  1. Data Cleaning

Next, you need to prepare the data for use by your ML model. The process involves tuning your data so that it is on your model. It entails cleaning, segmentation, labeling, normalization, and dealing with missing or inconsistent data.

You also eliminate redundancies, deal with categorical data and extract features. The cleaning process also helps deal with anomalies.

  1. Training and Evaluation

Here you select, train and validate your ML model. You feed your data to your different ML algorithms, and the algorithm tries to predict the desired outcome you wanted. The best way to go about this is to try several algorithms and pick the one that performs the best. You can do this by using the available performance metrics.

  1. Deployment

When you have established that the ML model is performing as expected, it is time to deploy your ML model. You can do this on your own or get professionals to incorporate your ML model into your business and handle your ML ops.

Remember, deployment is not the final step. You have to monitor and assess the real-time performance of your ml solution. 

Conclusion

Your business stands to benefit a great deal if you embrace ML and AI in your operations. ML can help you manage your inventory, understand customer patterns and engage better with customers.

As you develop your ML model for your business, pay attention to every step. The quality of your data will affect the quality of the results you get. 

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