Tutorial 1. Working with Watson Machine Learning engine
- Step 1: Credit risk prediction model creation, deployment as web-service and monitoring using Watson OpenScale - notebook
Tutorial 2. Working with Custom Machine Learning engine
- Step 1: Creation of Custom Machine Learning engine using Kubernetes cluster - deployment instruction
- Step 2: Data mart creation, model deployment monitoring and data analysis - notebook
Tutorial 3. Working with Azure Machine Learning Studio engine
- Step 1: Data mart creation, model deployment monitoring and data analysis - notebook
Tutorial 4. Working with Amazon SageMaker Machine Learning engine
- Step 1: Creation and deployment of credit risk prediction model - notebook
- Step 2: Data mart creation, model deployment monitoring and data analysis - notebook
Tutorial 5. Working with IBM SPSS C&DS engine
- Step 1: Data mart creation, model deployment monitoring and data analysis - notebook
Tutorial 6. Working with Watson Machine Learning engine on ICP
- Step 1: Credit risk prediction model creation, deployment as web-service and monitoring using Watson OpenScale - notebook
Tutorial 7. Working with not directly supported engine through Custom ML Engine
- Step 1: Credit risk model (scikit-learn) deployment on Azure ML Service - notebook
- Step 2: Creation of Custom Machine Learning engine and deployment on Azure Cloud as flask application - deployment instruction
- Step 3: OpenScale configuration to work with Custom ML Engine - notebook
- Step 4: Creation of scoring endpoint wrapper to automate payload logging on Azure ML Service - notebook