In a context of a binary classification, here are the main metrics that are important to track in order to assess the performance of the model. Confusion matrix The confusion matrix is used to have a more complete picture when assessing the performance of a model. ROC The receiver operating curve, also noted ROC, is the plot of TPR versus FPR by varying the threshold. Once the model has been chosen, it is trained on the entire dataset and tested on the unseen test set. Cross-validation Cross-validation, also noted CV, is a method that is used to select a model that does not rely too much on the initial training set.

"Some of the deep learning neural networks require tremendous amounts of computing power to train the systems," Elliot said. Properly planning for and designing an infrastructure that can support your specific environment's computational processing and storage requirements is critical to attaining AI's benefits. Furthermore, organizations may start too big, have the wrong data, or have a lot of data that's unusable, he said. Getting data assets and capabilities in place will deliver a huge lift to any planned AI project. Take a look at your talent.

To succeed in machine learning, we must do a decent amount of prep work. Just adding data, data, data can lead to false signals and invalid correlations. We can end up missing the signal in all the noise. In "Why Machine Learning Works," computer scientist George Montañez walks the reader through the prerequisites for successful machine learning. He notes that, at its core, machine learning is a form of search algorithm.