Machine learning inference is the process of running live data points into a machine learning algorithm or model to calculate an output such as a single numerical score, a string of text, an image, or any other structured or unstructured data. It involves deploying a software application into a production environment, as the ML model is typically just software code that implements a mathematical algorithm. The ML inference process deploys this code into a production environment, making it possible to generate predictions for inputs provided by real end-users. The machine learning life cycle includes two main parts: the training phase and the machine learning inference phase. The training phase involves creating a machine learning model, training it by running the model on labeled data examples, then testing and validating the model by running it on unseen examples. Machine learning inference involves putting the model to work on live data to produce an actionable output. Successful ML deployments often are the result of tight coordination between different teams, and newer software technologies are also often deployed to try to simplify the process.