Читать книгу Enterprise AI For Dummies - Zachary Jarvinen - Страница 37

In the prediction stage, the system uses the model to process new data (not historical data), detect patterns and trends, and attempt to match them to patterns from the learning data.

TABLE 1-3 Machine Learning as a Recipe

	Machine Learning	Recipe
Task	An algorithm is a step-by-step instruction set or formula for solving a problem or completing a task.	Thaw the chicken. Season the chicken. Bake the chicken at 350°F.
Objective	Minimize errors (loss function) to attain the best approach to solve a task.	Minimize the number of ingredients and steps required to prepare a tasty dish.
Insight/result	The algorithm learns from errors, finds the best approach, and generates insights and rules used to make predictions.	Learn from your mistakes the next time you attempt the recipe.

For example, if you process a brochure for the San Diego Zoo using the model, it would recognize the content about elephants and add the tag “elephant” to the document along with a score. The result is a prediction in the form of the percentage probability that the document contains information about elephants. Basically, the model makes a data-driven guess.

In AI and data science, execution is not just implementing a plan. The methodology establishes an iterative process of learning, discovering, and then acting based on new information as opposed to a more traditional IT model of formulating a plan or idea and then rolling it out as planned.