Azure AI Workflow and Pipelines
Azure Training Architect II
Artificial Intelligence, or AI, and the Machine Learning that drives it, is one of the most exciting new frontiers of technology being presented in the Cloud today. Microsoft's AI-100 is a certification path and exam that covers the Azure AI services and products available.
The Certification, as described by Microsoft:
Candidates for this exam analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements.
Candidates translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. Candidates design and implement AI apps and agents that use Microsoft Azure Cognitive Services and Azure Bot Service. Candidates can recommend solutions that use open source technologies.
Candidates understand the components that make up the Azure AI portfolio and the available data storage options.
Candidates implement AI solutions that use Cognitive Services, Azure bots, Azure Search, and data storage in Azure. Candidates understand when a custom API should be developed to meet specific requirements.
The third course covering the AI-100 discusses the path data takes through an AI design, and what regulations and governance apply to that data.
An overview of what can be expected from the third of our five part series on the AI-100 Certification Exam.
About the Training Architect
Meet the Training Architect, Dan Sasse https://www.linkedin.com/in/danielsasse/
Design AI Solutions (continuing)
Design for Data Governance, Compliance, Integrity, and Security
Define How Users and Applications Authenticate to AI Services
In this lesson, we review Azure AD application registration. We cover how this can be used to authenticate users and applications for and to an AI Service. Further reading documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/authentication https://docs.microsoft.com/en-us/azure/active-directory/develop/authentication-scenarios https://docs.microsoft.com/en-us/azure/active-directory/develop/v2-overview
Design a Content Moderation Strategy for Data Usage within an AI Solution
This lesson centers around the Content Moderator Cognitive Service. We detail its components and functions as well as how it can be used as more than a front-line moderation tool. We cover using it as a filter for ensuring data retention within business policy, operating behind other services. Further reading documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/overview https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/review-api
Ensure that Data Adheres to Compliance Requirements Defined by an Organization
This brief lesson reiterates that Azure Policy is the go-to tool for defining and enforcing an internal or self-defined policy that needs to be considered for an AI Design. Further reading documentation: https://docs.microsoft.com/en-us/azure/governance/policy/how-to/get-compliance-data https://docs.microsoft.com/en-us/microsoft-365/compliance/compliance-manager-overview
Ensure Appropriate Governance for Data
In this lesson, we discuss overarching data governance and the tools Azure provides to address this design need. Further reading documentation: https://docs.microsoft.com/en-us/azure/governance/management-groups/overview https://docs.microsoft.com/en-us/azure/governance/blueprints/overview
Design Strategies to Ensure the Solution Meets Data Privacy and Industry Standard Regulations
This lesson steps away from discussing specific technical services and instead reviews overall design considerations important for delivering sound AI solutions regarding data privacy as well as industry standards and governance. Further reading documentation: https://azure.microsoft.com/en-us/support/legal/cognitive-services-compliance-and-privacy/ https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-retention-privacy https://docs.microsoft.com/en-us/azure/cognitive-services/bing-web-search/use-display-requirements
Implement and Monitor AI Solutions
Implement an AI Workflow
Develop AI Pipelines
This lesson covers the various components of a pipeline built around an AI or machine learning solution, and what Azure services could be used to act as those various components. Further reading documentation: https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture
Manage the Flow of Data Through Solution Components
In this lesson, we review event generation, stream analytics, and how these concepts can be leveraged to keep an eye on the data moving between the different pieces of an AI solution. Further reading documentation: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-introduction https://docs.microsoft.com/en-us/azure/iot-fundamentals/iot-introduction https://docs.microsoft.com/en-us/azure/iot-edge/about-iot-edge
Implement Data Logging Processes
This lessons centers around the different intervals used to generate logs during an AI or machine learning project. We also take a general look into what logs are generated and when. Further reading documentation: https://docs.microsoft.com/en-us/azure/azure-monitor/log-query/get-started-portal https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-track-experiments https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-monitor-resource-health
Define and Construct Interfaces for Custom AI Services
This lesson briefly reviews the options an AI engineer has for tailoring and making cosmetic changes to the web interface of an AI or ML deployment. Further reading documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home https://docs.microsoft.com/en-us/azure/cognitive-services/bing-custom-search/tutorials/custom-search-web-page
Integrate AI Models with Other Solution Components
In this lesson, we look at and review several general-purpose integration services that Azure has available for a well-crafted AI design to take advantage of. Further reading documentation: https://docs.microsoft.com/en-us/azure/event-grid/compare-messaging-services https://docs.microsoft.com/en-us/azure/api-management/api-management-key-concepts
Design Solution Endpoints
In this lesson, we review what an AI solution endpoint is and what Azure services are available to integrate with it. Further reading documentation: https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-for-kafka-ecosystem-overview https://docs.microsoft.com/en-us/rest/api/cognitiveservices/ https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
Develop Streaming Solutions
In this lesson, we review the components of a standard, Azure-based IoT streaming solution and discuss how these services could integrate with an AI design. Further reading documentation: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-solution-patterns https://docs.microsoft.com/en-us/power-bi/service-azure-and-power-bi https://docs.microsoft.com/en-us/azure/iot-hub/about-iot-hub
Where do you go from here?
Good Work! Now: on to the next Course Segment!
Well done on completing Course Segment 3, Workflow and Pipelines, in our Certification Review of the Azure AI-100. We'd appreciate some feedback before you move on to Segment 4; Implementation and Monitoring.
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