The mlrequest API integrates machine learning without servers, dependencies, or maintenance. Developers can make a request to a predict or to a learn endpoint to get started. Models are duplicated across 5 data centers around the world with latency-routed requests at less than 60ms response time.
The following is a list of sample source code snippets that matched your search term. Source code snippets are chunks of source code that were found out on the Web that you can cut and paste into your own source code. Whereas most of the sample source code we've curated for our directory is for consuming APIs, we occasionally find something interesting on the API provider side of things. If you know of some sample source code that would be of interest to the ProgrammableWeb community, we'd like to know about it. Be sure to check our guidelines for making contributions to ProgrammableWeb.
The mlrequest Reinforcement Learning Python Sample Code demonstrates how to call a reward endpoint after making a prediction, if the model's prediction resulted in a positive outcome. For example, if a user started watching the movie that was recommended, the reward endpoint should then be called to notify the model that it predicted a good action.
The mlrequest Learning Python Sample Code demonstrates how a machine can learn labels. To learn, send the labeled data to a /learn endpoint. The act of a model learning from data is referred to as training or updating.
The mlrequest Evaluating Python Sample Code demonstrates how well the model predicted Iris flower types to the actual labels for each example. This is after a developer has received predictions from the unseen data, and now wants to know how well the model did.