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