AI-100: Designing and Implementing an Azure AI Solutions

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AI-100: Designing and Implementing an Azure AI Solutions

Free Courses : AI-100: Designing and Implementing an Azure AI Solutions

UPDATE : Please note that this course will be upgraded to AI 102 with the new curriculum.

This means that even if you are preparing for AI 100, you can continue to use this course for AI 102 preparation.


Microsoft Azure offers a spread of services designed to work together to enable rapid development of high-performance AI solutions. This skill teaches how these Azure services work together to enable you to design, implement, operationalize, monitor, optimize, and secure your AI solutions on Microsoft Azure. This path is designed to address the Microsoft AI-100 certification exam.

This course covers Azure Cognitive APIs for Visual Features including Face Detection, Tagging the content of an image, OCR as well as Text Analytics for Language Detection, Sentiment Analysis and Key Phrase extraction. The course is very hands on and covers the implementation of these APIs using Python as well as Javascript.

With cognitive services you will be able to build all such or even more types of applications.

Here is the course content covered in this course :

Analyze solution requirements (25-30%)

Recommend Azure Cognitive Services APIs to meet business requirements

select the processing architecture for a solution

select the appropriate data processing technologies

select the appropriate AI models and services

identify components and technologies required to connect service endpoints

identify automation requirements Map security requirements to tools, technologies, and processes identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements

identify which users and groups have access to information and interfaces

identify appropriate tools for a solution

identify auditing requirements Select the software, services, and storage required to support a solution

identify appropriate services and tools for a solution

identify integration points with other Microsoft services

identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design AI solutions (40-45%)

Design solutions that include one or more pipelines

define an AI application workflow process

design a strategy for ingest and egress data

design the integration point between multiple workflows and pipelines

design pipelines that use AI apps

design pipelines that call Azure Machine Learning models

select an AI solution that meet cost constraints Design solutions that uses Cognitive Services

design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs Design solutions that implement the Microsoft Bot Framework

integrate bots and AI solutions

design bot services that use Language Understanding (LUIS)

design bots that integrate with channels

integrate bots with Azure app services and Azure Application Insights Design the compute infrastructure to support a solution

identify whether to create a GPU, FPGA, or CPU-based solution

identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure

select a compute solution that meets cost constraints Design for data governance, compliance, integrity, and security

define how users and applications will authenticate to AI services

design a content moderation strategy for data usage within an AI solution

ensure that data adheres to compliance requirements defined by your organization

ensure appropriate governance of data

design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions (25-30%)

Implement an AI workflow

develop AI pipelines

manage the flow of data through the solution components

implement data logging processes

define and construct interfaces for custom AI services

create solution endpoints

develop streaming solutions Integrate AI services and solution components

configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs

configure integration with Azure Cognitive Services

configure prerequisite components to allow connectivity to the Microsoft Bot Framework

implement Azure Cognitive Search in a solution Monitor and evaluate the AI environment

identify the differences between KPIs, reported metrics, and root causes of the differences

identify the differences between expected and actual workflow throughput

maintain an AI solution for continuous improvement

monitor AI components for availability

recommend changes to an AI solution based on performance data

Hope this course would be informative to you. Please reach out to me if you have any questions.

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