When solving machine learning problems, it's important to take time to analyze both the data and the possible amount of work beforehand. This preliminary step is flexible and less formal than all the subsequent ones on this list.
From the definition of machine learning, we know that our final goal is to make the computer learn or generalize a certain behavior or model from a sample set of data. So, the first thing we should do is understand the new capabilities we want to learn.
In the enterprise field, this is the time to have more practical discussions and brainstorms. The main questions we could ask ourselves during this phase could be as follows:
- What is the real problem we are trying to solve?
- What is the current information pipeline?
- How can I streamline data acquisition?
- Is the incoming data complete, or does it have gaps?
- What additional...