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AI-102: How to Automate Azure AI Model Deployment with CI/CD Workflows?

By: Author Alex Lim

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Categories Exam

Learn how to streamline Azure AI model deployment using CI/CD workflows. This guide is perfect for preparing for the AI-102 exam and mastering Azure AI services for custom speech recognition.

Table of Contents

Question

Your organization, Xerigon Corporation, is developing an AI-driven application that utilizes Azure AI services for custom speech recognition.

You want to automate the deployment of new models and updates to the AI service and ensure the availability of the endpoint for the best-performing custom speech model. What should you do?

A. Implement a container deployment.
B. Create an Azure AI service resource.
C. Implement the continuous integration/continuous delivery (CI/CD) workflows.
D. Determine a default endpoint for the AI service.

Answer

C. Implement the continuous integration/continuous delivery (CI/CD) workflows.

Explanation

You would implement the continuous integration/continuous delivery (CI/CD) workflows in the given scenario. This is crucial for automating the deployment of new models and updates to the AI service. CI/CD pipelines enable continuous integration of changes and automated deployment to production environments, ensuring that the latest models are available and the service remains up to date with minimal manual intervention. You can use the Azure CLI and Azure AI Speech CLI for CI/CD automation workflows for custom speech.

Below are the outlined steps you would follow to integrate Azure AI services into the CI/CD pipeline:

  1. To manage your AI models, use a version control system such as Git.
  2. Implement a build automation tool such as Azure Pipelines or Jenkins to automatically trigger builds whenever changes are pushed to the version control system.
  3. Train your AI models using Azure Machine Learning, and deploy them as web services or containers.
  4. Automate the testing of AI models, services, and APIs to ensure they meet quality standards.
  5. Use your CI/CD pipeline to deploy trained AI models and services to production environments.
  6. Implement monitoring and logging for AI models and services to track performance and gather feedback.

You would not implement a container deployment in the given scenario. This involves packaging the application and its dependencies into a container image which can then be deployed consistently across different environments. A container deployment will not provide any automation.

You would not determine a default endpoint for the AI service in the given scenario. This ensures that applications can consistently access the service. It is important for configuration and connectivity. However, it does not automate the deployment of new models and updates to the AI service and ensure the availability of the endpoint for the best-performing custom speech model.

You would not create an Azure AI service resource in the given scenario. This is a fundamental step in utilizing Azure AI services, as it sets up the necessary infrastructure to host AI models. However, creating the resource alone does not automate the deployment of new models and updates to the AI service and ensure the availability of the endpoint for the best-performing custom speech model.

Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.