IN PROGRESS
Module 1: Doing Data Science on Azure
Module Objectives:
- Explain the steps involved in the Data Science Process
- Explain the machine learning modeling cycle
- Explain data cleansing and preparation
- Explain model feature engineering
- Explain model training and evaluation
- Explain about model deployment
- Understand the specialized roles in the data science process
Module 2: Doing Data Science with Azure Machine Learning service
Module Objectives:
- Explain the difference between Azure Machine Learning Studio and Azure Machine Learning service
- Explain how Azure Machine Learning service fits into the data science process
- Explain the concepts related to an Azure Machine Learning service experiment
- Explain the Azure Machine Learning service pipeline
- Train a model using Azure Machine Learning service
Module 3: Automate Model Selection with AML Service
Module Objectives:
- Explain the machine learning pipeline
- Explain Azure Machine Learning service AutoML
- Create a Python script that uses the Azure Machine Learning service’s AutoML to recommend a model
- Test the recommended model from your Python script
Module 4: Manage and Monitor Models
Module Objectives:
- Register a model in the Azure Machine Learning service Model Registry
- Query information about the registered model
- Register an image containing a machine learning model
- Install the Azure ML Monitoring SDK
- Enable model data collection using the portal and using Python with the SDK
- Use the Azure ML Monitoring SDK to collect and monitor model data
Module 1: Doing Data Science on Azure
Module Objectives:
- Explain the steps involved in the Data Science Process
- Explain the machine learning modeling cycle
- Explain data cleansing and preparation
- Explain model feature engineering
- Explain model training and evaluation
- Explain about model deployment
- Understand the specialized roles in the data science process