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Microsoft Cloud AI Power Workshop for Professional AI Developers (AIBC-PD)

Course Description Schedule

Course Overview

Master your skills in Microsoft’s AI-oriented services.

This workshop offers 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.

Who should attend

This workshop is intended for AI Developers on Azure.

Course Objectives

  • 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.

Course Content


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
  • Lab 4.5: Collect and Analyzing Data from a scoring service
  • Summary and White-board Discussion
Classroom Training
Modality: C

Duration 2 days

Dates and Booking

Currently there are no training dates scheduled for this course.  Enquire a date


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