Training an ML model nowadays is the easy part — managing the lifecycle of the experiments and the model is where things get complicated. Luckily, Weights and Biases provide the developer tools that with a couple lines of code let you keep track of hyperparameters, system metrics, and outputs so you can compare experiments, and easily share your findings with colleagues. However, the value of the model comes from operationalising and turning the model into a prediction service. This requires making data available to these services consistently to how the models were trained.

Hopsworks 3.0 introduced a new Feature View abstraction and now supports KServe for model serving. Together these two features provide the APIs to consume data from the feature store consistently between training and production and allow you to deploy models from your Weights and Biases model registry into production quickly, providing a Rest Endpoint to perform prediction requests against.