About the course
- Understand and use the Team Data Science Process (TDSP) to clearly define business goals and success criteria
- Use a code-repository system with the Azure Machine Learning Workbench using the TDSP structure
- Create an example environment
- Use the TDSP and AMLS for data acquisition and understanding
- Use the TDSP and AMLS for creating an experiment with a model and evaluation of models
- Use the TDSP and AMLS for deployment
- Use the TDSP and AMLS for project close-out and customer acceptance
- Execute Data preparation workflows and train your models on remote Data Science Virtual Machines (with or without GPUs) and HDInsight Clusters running Spark
- Manage and compare models with Azure Machine Learning
- Explore hyper-parameters on Spark using Azure Machine Learning
- Leverage Batch AI training for parallel training on GPUs
- Deploy and Consume a scoring service on Azure Container Service
- Collect and Analyze data from a scoring service in production to progress the data science lifecycle.
Content sourced by Microsoft
Prerequisites
Important! You must complete the following installations and requirements before attending the boot camp:
Modules
Introduction and Context
Lab 3.1: Introduction to Team Data Science Process with Azure Machine Learning
Lab 3.2: Comparing and Managing Models with Azure Machine Learning
Lab 3.3: Deploying a data engineering or model training workflow to a remote execution environment
Lab 3.4: Managing conda environments for Azure Machine Learning workflows
Summary and White-board Discussion
Introduction and Context.
Lab 4.1: Explore hyper-parameters on Spark using Azure Machine Learning
Lab 4.2: Leverage Batch AI Training for parallel training on GPUs
Lab 4.3: Deploying a scoring service to Azure Container Service
Lab 4.4: Consuming the final service
- 3:00-3:50: Lab 4.5: Collect and Analyzing Data from a scoring service
-Summary
