What are the reasons to use machine learning?
An example of a business problem where the use of ML would be appropriate is generating personalized recommendations. In this case, the solution to the problem requires complex logic, and we would want to provide personalized recommendations at scale with quick turnaround times.
Requires complex logic
- Since developing personalized recommendations requires complex logic, ML is an appropriate tool to consider.
Requires scalability
- Serving millions of requests for personalized recommendations every second is a challenge.
Requires personalization
- Delivering personalized recommendations at scale and being responsive at the same time is difficult to achieve with classical programming techniques.
Requires responsiveness
- The ability to deliver personalized recommendations within a few seconds even while handling millions of requests per second is expected.
What are the reasons to NOT use machine learning?
Business reasons to avoid ML depend on whether traditional methods and rules are viable options, if there are few or no requirements to adapt to new data, if business goals include 100% outcome accuracy, or if models must be explained or translated.
Can be solved with traditional algorithms
- If the problem is not overly complex, an ML solution might be overcomplicated
Does not require adapting to new data
- If data and conditions are not changing, a more traditional approach could be more appropriate.
Requires 100% accuracy
- ML predictions often provide less than 100% accuracy.
Requires full interpretability
- If being able to explain what is going to happen if you change the parameters or input is a priority, ML might not be the best solution.