AI-100: Designing & Implementing Azure AI Solutions- AI-100T01-A Training Course

Last updated

Course Code

azureai

Duration

28 hours (usually 4 days including breaks)

Requirements

  • An understanding of Microsoft Azure Cloud 
  • Experience with using Azure, fundamental data processing
  • Awareness of Artificial Intelligence

Audience

  • Data practioners
  • Artificial Intelligence enthusiasts
  • Data Engineers
  • Cloud practioners

Overview

Microsoft Azure Cloud offerings are the most comprehensive set of services w.r.t. AI, ML, DL

From Vision, Speech to Language to Conversational bots, Azure is empowering organizations to process their data to derive insights

This instructor-led, live training (online or onsite) is aimed at AI enthusiasts who wish to use Azure to build AI scenarios in the cloud

By the end of this training, participants will be able to:

  • Build Azure based AI scenarios.
  • Understand the end to end functioning of API based infering
  • Build conversational bots for business needs

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request more details or customized training for this course, please contact us to arrange.

Course Outline

Day 1:

Module 1: Azure Fundamentals

  1. What’s Cloud Computing?
  2. Introduction to Azure & Azure account
  3. Azure portal & services overview- compute & storage
  4. Security, responsibility, policies and trust in Azure
  5. Cost estimation in Azure

Module 2: Building Intelligent Cloud Applications

  1. Serverless architectures
  2. Leveraging pretrained models
  3. Continuous Delivery- scaling, security, monitoring

Day 2:

Module 3: Intro to AI on Azure

  1. AI solutions & services in Azure
    • Cognitive Services APIs
    • Bot Framework
    • Machine Learning services in Azure

Module 4: Hands on with Machine Learning Studio

  1. ML Studio account deep dive
  2. Building ML Studio experiments
  3. Training- Testing and Publishing ML Studio experiments
  4. Infering models as Web services- Batch and Real time
  5. Business Scenario hands on using ML Studio

Day 3:

Module 5: Azure Cognitive Speech Services

  1. Using Cognitive Services Speech to Text translation
  2. Speaker Recognition API usage
  3. Business Scenario deep dive using Speech Services

Module 6: Azure Cognitive Vision Services

  1. Face detection and recognition using Computer Vision API
  2. Image processing with Computer Vision
  3. Image classification with Custom Vision
  4. Business scenario deep dive using Computer Vision

Day 4:

Module 7: Creating Intelligent Bots using Azure Bot service

  1. Natural interaction bot using Cognitive Services
  2. Bot Integration with Speech, Search, Language Understanding, QnA, Vision
  3. Bot deployment connecting to external channels
  4. Business scenario implementation using Bot service 

  Summary and Conclusion

Testimonials

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