proaidev_bootcamp.md

Welcome

Welcome to the second two days of the Cloud AI Bootcamp. In these two days, we will focus on hands-on activities that develop proficiency in AI-oriented workflows leveraging Azure Machine Learning Workbench and Services, the Team Data Science Process, Visual Studio Team Services, Azure Batch AI, and Azure Container Services. These labs assume a introductory to intermediate knowledge of these services, and if this is not the case, then you should spend the time working through the pre-requisites.

Pre-requisites

Pre-requisites can be found here. Briefly, pre-requisites include the following:

Resources

• The ability to create resources within an Azure subscription
• Familiarity with how to create resources in said subscription

Azure Machine Learning Services

• Python Proficiency and familiarity with data science workloads and techniques.

Version Control

• Familiarity with Git and a Visual Studio Team Services Account to leverage collaborative features of Azure Machine Learning.

Languages

• Python

Goals

• 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
• 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.

Agenda

Please note: This is a rough agenda, and the schedule is subject to change pending class activities and interaction.

• Day 1
  • 9-11: Introduction and Context
  • 10-11: Lab 3.1: Introduction to Team Data Science Process with Azure Machine Learning
  • 11-12: Lab 3.2: Comparing and Managing Models with Azure Machine Learning
  • 12-1: Lunch
  • 1-2:20 Lab 3.3: Managing conda environments for Azure Machine Learning workflows
  • 2:30-3:50 Lab 3.4: Executing a data engineering or model training workflow to a remote execution environment
  • 4-5: Summary and White-board Discussion
• Day 2
  • 9-10: Introduction and Context.
  • 10-11: Lab 4.1: Managing Models using Azure Machine Learning
  • 11-12:00: Lab 4.2: Deploying a scoring service to Azure Container Service (AKS)
  • 12:00-1: Lunch
  • 1:00-1:50: Lab 4.3: Consuming the final service
  • 2:00-2:50: Lab 4.4: Collect data from a scoring service
  • 3:00-3:50: Lab 4.5: Explore hyper-parameters on Spark using Azure Machine Learning (if time)
  • 4:00-5:00: Q&A and Feedback for Pro AI Bootcamp

Discussion Forum

We will also use a gitter forum for discussion. Please post comments and questions here.