Читать книгу Artificial Intelligence for Business - Jason L. Anderson - Страница 15

With your project plan and data defined, it is time to start building an initial version your system. As with any project, it is best to take an iterative approach. In the prototype step, you will select a subset of your use cases to validate the idea. In this way, you are able to see if the expected value is materializing before you are completely invested. This step also enables you to adjust your approach early if you see any problems arise. Developing a prototype will help you to see, with actual results, whether the ideas and plans you defined in the previous steps have promise. In the event that they do not, you should be able to recover quickly and adjust them using the knowledge gained from prototyping, without the wasted investment of building a full system.

During the prototyping phase, it is necessary to have realistic expectations. With most AI systems, they improve with more data and parameter tweaking, so you should expect to see increasing improvements over time. Luckily, metrics like precision and recall can be empirically measured and used to track this improvement. We will also cover the cases when more data is not the answer and what other techniques can be pursued to continue improving the system.