How Speed of Predictions Influences Their Trustworthiness
Speed in machine learning can refer to two time periods: how long it takes to build a model and how long it takes to use a model to score a prediction. Although expediting model development time is a key concern for getting a model into use and delivering value, the focus for model performance is instead on the time to score a prediction.
What Types of Prediction Scenarios Take Place in Machine Learning?
There are two most typical paradigms for model scoring.
It’s important to evaluate the accuracy of your model throughout the development process. Find out how to best leverage metrics and visualizations to evaluate whether you’ve achieved accuracy befitting a production-ready AI system.
Robustness and Stability
A model in production encounters all sorts of unclean, chaotic data—from typos to anomalous events—which can trigger unintended behavior. Find out how to test whether your model is ready for the real world.