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Posted: August 4th, 2022
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In my opinion, it’s not possible that companies avoid machine learning because they do not understand how it works, or feel uncomfortable about the results it produces. This is because machine learning matters. We are living in an era where data is very important and the popularity of data mining has increased immensely. Things such as increasing volumes and arrays of data available, computational processing that is less costly and more potent, and inexpensive data storage are the order of the day (Zhang & Ma, 2019). All these things mean that companies will increasingly need to automatically generate frameworks that can assess larger, more intricate data and convey faster, more accurate results-even on a very big scale. Moreover, by creating models that are accurate, a company has a better likelihood of recognizing opportunities that are profitable-or avoiding unidentified risks. This is where machine learning comes into play. The iterative facet of this technology is vital because as models get exposure to new data, they will be able to adapt autonomously. They will learn from past calculations to generate dependable, recurrent results and decisions (Michalski, Carbonell & Mitchell, 2013).
Therefore, in order to fix the issue of lack of understanding of how machine learning works, companies need to be more patient, apply creativity, and experiment more with the technology. The successful use of machine learning by companies like Google, Yelp, Facebook, Twitter, Pinterest, Baidu, HubSpot, IBM and Salesforce is an indication that machine learning can be understood and as a result, bring success to a business. However, with machine learning, companies should be ready to deal with issues like the excessive focus on theories and algorithms, and algorithms becoming obsolete as data grows.
References
Michalski, R., Carbonell, J., & Mitchell, T. (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
Zhang, C., & Ma, Y. (2019). Ensemble machine learning: Methods and applications. Springer Science & Business Media.
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