The Hidden Limitations of Standard Machine Learning Models in Business: What You Need to Know
Machine learning and AI have rapidly become buzzwords in the business world, with companies of all sizes and industries eager to leverage the benefits of data-driven decision-making. However, while the potential applications of machine learning are virtually limitless, there are also some major disadvantages to standard, off-the-shelf machine learning models that business leaders need to be aware of. In this blog post, we will explore the key disadvantages of standard machine learning models and discuss the implications for businesses looking to adopt these technologies.
Limited Data Diversity One of the biggest limitations of standard machine learning models is that they can only be as good as the data that is fed into them. If the data set used to train a model is not representative of the real-world problem it is meant to solve, the model will struggle to make accurate predictions and may even produce biased results. For example, if a machine learning model is trained on a data set that only includes information about male customers, it will likely perform poorly when it comes to predicting behavior for female customers. This is because the model has not been exposed to a diverse range of inputs and will therefore lack the necessary context to make accurate predictions.
Black Box Decision-Making Another major disadvantage of standard machine learning models is that they are often referred to as "black box" systems. This means that while the model may be able to make accurate predictions, it is difficult to understand exactly how those predictions are being made. This lack of transparency can make it difficult for business leaders to trust the results produced by machine learning models, especially in sensitive areas such as medical diagnoses or hiring decisions.
Overfitting and Underfitting Overfitting and underfitting are two common problems that arise when training machine learning models. Overfitting occurs when a model is too closely tailored to the training data and is unable to generalize to new data. This means that while the model may perform well on the training data, it will perform poorly on new, unseen data. On the other hand, underfitting occurs when a model is too simple to capture the complexity of the data and is unable to make accurate predictions. Both of these problems can lead to poor performance and reduced accuracy for machine learning models in business.
Cost and Complexity While the benefits of machine learning are significant, the technology can also be expensive and complex to implement directly. Businesses need to invest in specialized hardware and software, as well as hire trained data scientists and engineers to build and maintain machine learning models. This can be a significant barrier for smaller companies, who may not have the resources or expertise to invest in machine learning.
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