The first thing to think about is whether or not your task is well suited to machine learning.
Do you have a well-defined problem with clear inputs and outputs? It is essential that you have a clear idea of what your model would have as inputs and outputs. Otherwise, you may have a difficult time in the feature engineering and evaluation stages of producing a machine learning model.
Do you have metrics that you can use to evaluate a model’s performance and to compare different models? Without an easy way to evaluate models, you will have a difficult time determining whether your model was successful, and in choosing which model to use. You will also have a difficult time iterating upon, and improving, your model as your use-case evolves over time.
Does the problem require an approximate solution? Most machine learning algorithms are used in situations where there is no exact way to find a solution, or the exact solution is too costly to implement. If your problem does have a method to solve it exactly, such as through the use of regular expressions, classical optimization techniques such as linear programming, or older AI techniques such as constraint satisfaction problems, then you may be better off using these methods instead.
Does the problem fit the machine learning paradigm? Most machine learning algorithms rely on the idea that current data will be useful in predicting or classifying future data. If your situation is prone to external events invalidating previous data, then machine learning will most likely not be effective. Similarly, if previous data has no relevance to future data, your model will not learn any useful trends that help you understand incoming data in a real-world setting. It is essential that your model sees relevant past data in order to use machine learning effectively.
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