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AI-100 Azure AI Engineer Associate, Part 4 of 4: Implement AI Solutions

with expert Anand Rao


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Course at a glance

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Release date 4/6/2021
Level Advanced
Runtime 1h 2m
Closed captioning N/A
Transcript N/A
eBooks / courseware N/A
Hands-on labs N/A
Sample code N/A
Exams N/A


Enterprise Solutions

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Course description

Azure AI Engineers work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions. This course covers Implement and monitor AI solutions which covers (25-30%) of the exam.

Prerequisites

Candidates for this certification should be proficient in C#, Python, or JavaScript and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure. They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Meet the expert

Anand Rao is a senior technical instructor and cloud consultant. He has worked with large enterprises for about 15 years and has a wide range of technologies in his portfolio.Anand Rao has delivered instructor led trainings in several states in India as well as several countries like USA, Bahrain, Kenya and UAE. He has worked as a Microsoft Certified Trainer globally for Corporate Major Clients.

Course outline



Implement and Monitor AI Solutions

Develop AI Pipelines (22:19)
  • Introduction (00:08)
  • Develop AI Pipelines (05:00)
  • Manage the Flow of Data Through Solution Componen (03:12)
  • Implement Data Logging Processes (02:32)
  • Define and construct interfaces for custom AI ser (08:12)
  • Integrate AI Models with Other Solution Components (03:05)
  • Summary (00:08)
Design Solution Endpoints (16:26)
  • Introduction (00:08)
  • Create Solution Endpoints (02:00)
  • Develop streaming solutions (05:11)
  • Configure Prerequisite Components and Input Datas (03:08)
  • Configure Integration with Azure Services (02:14)
  • Implement Azure Search in a Solution (03:35)
  • Summary (00:08)
Differences Between KPIs and Reported Metrics (24:07)
  • Introduction (00:08)
  • Differences of KPIs and Reported Metrics (03:37)
  • Expected and Actual Workflow Throughput (03:52)
  • Implement AI for Continuous Improvement (03:22)
  • Monitor AI Components for Availability (06:57)
  • Recommend Changes to an AI Solution Based on Perf (05:07)
  • Course Conclusion - AI 100 (00:53)
  • Summary (00:08)