Tutorial 1A: Intro to ML Model Deployment and Management
In this tutorial, you will launch the MLOps test drive from Aquarium and access the URL, which takes you to the H2O.ai Platform Studio, where you will launch Driverless AI to create a project that is shared with MLOps. In MLOps, you will deploy one of the Machine Learning Models. After the model is deployed, you will make scoring requests and get a score result using curl. For our particular use case, we will deploy a hydraulic model that predicts hydraulic cooling conditions. The result will be percentage values for 3, meaning the hydraulic operates close to total failure, 20 meaning it operates at reduced efficiency, and 100 meaning it operates at full efficiency.