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When should a company apply Machine Learning techniques?
There are several main reasons to implement Machine Learning solutions, which work better when:
- Human can do it but cannot explain it how they did it. In order to automate or scale tasks done by humans, it is essential to understand set of rules or steps human taken in order to automate a process. However, in absence of set of programmable rules explaining how we do it. Machine learning algorithms can learn the rules from the sample tasks performed by humans.
- Human expertise are rare or expensive. This is the case with the medical diagnoses where rather than having a doctor analyze every single x-ray, for example, we can use a machine-learned algorithm to do the work at a fraction of the cost. Expenses can also imply time involved in implanting AI/ML models. Any human can label images with appropriate tags, but if your company has hundreds of millions of them to label, even a few seconds equates to dozens of years of person-time to do the labeling.
- In case your existing solution could be significantly more profitable with small increases in accuracy, Machine Learning can provide that increase. For instance, this was once the case with handwritten digit recognition, e.g., the US postal service estimated that a single percent increase in accuracy from their current automated system would save them some hundreds of millions of dollars. The Machine Learning methods applied were able to provide this small improvement that saved them a significant amount of money.