(Valid For First 1000 Enrollment)
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.
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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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