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Private Onsite | 2 Days Classroom

AI Bootcamp for Professional AI Developers

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.

Upcoming Dates

There are no scheduled classes for this course at the moment. Please, send us an information request to find out more.

Course Delivery Options

Private Onsite.

The course will take place in your company’s facilities. We limit attendance to no more than 20 students in order to maintain a good level of interactivity.
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2 Days Classroom.

The course will take place in a classroom with no more than 20 students in order to maintain a good level of interactivity.

View outline
Pre-requisites

Important! You must complete the following installations and requirements before attending the boot camp:

In preparation for the upcoming Machine Learning Boot Camp, there are a few things you need to do to be able to take the class. These activities take about 2.5 hours in total, and must be completed prior to showing up for the event. Failure to do so will put you begind the rest of the class and divert your time and attention from the material covered throughout the bootcamp. As proof you have completed the steps below, you must bring the “Web Service ID” code (described below) with you to sign-in at the event.

What you will need:

  • A Microsoft Azure account where you can create resources (Either an organization account, an MSDN subscription account, a Trial Account, or an account provided by your company).
  • A Microsoft Azure Machine Learning Experimentation and Model Management Account.
  • A local Windows laptop where you can install software or a Windows Data Science Virtual Machine (Size D4sV3 minimum is required).
    • After this 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.

    Course Outline
    Module 1. Introduction and context
    Labs

  • 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

    Business Case 1

    • Module 2. Introduction and context
    Labs

  • 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

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