LUIS Container Image and Usage
Question
The LUIS container loads your trained or published Language Understanding model.
It provides access to the query predictions from the container's API endpoints.
You plan to use them in your environment to comply with your organization's security and compliance requirements.
As a prerequisite, you have a docker engine installed and configured on your host computer.
It is also connected to Azure for billing purposes.
You also have an Azure Cognitive Services resource and trained or published LUIS app packaged as a mounted input to the container.
Review the steps given below and sequence them to get the container image and use it on your host computer: Step 1: Query the container's prediction endpoint Step 2: Use the LUIS portal to export the trained or published application package Step 3: Move package file into the required input directory on the host computer Step 4: Run the container, with the required input mount and billing settings Step 5: Get the docker image using docker pull Step 6: Import the endpoint logs for active learning Step 7: Log user queries to enable active learning.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B
The correct sequence of steps is as given below:
To learn more about installing and running containers for LUIS, use the link given below:
The correct sequence of steps to get the LUIS container image and use it on the host computer is as follows:
B. Step 5 -> Step 2 -> Step 3 -> Step 4 -> Step 1 -> Step 6 -> Step 7
Here's a detailed explanation of each step:
Step 5: Get the docker image using docker pull First, you need to download the LUIS container image from Docker Hub using the docker pull command. This will ensure that you have the latest version of the LUIS container image.
Step 2: Use the LUIS portal to export the trained or published application package Next, you need to use the LUIS portal to export the trained or published application package. This package contains your Language Understanding model and can be downloaded as a JSON file.
Step 3: Move package file into the required input directory on the host computer After exporting the application package, you need to move the package file into the required input directory on the host computer. This directory will be mounted as an input to the container when it is run.
Step 4: Run the container, with the required input mount and billing settings Now, you can run the LUIS container with the required input mount and billing settings. This will start the container and provide access to the API endpoints.
Step 1: Query the container's prediction endpoint Once the container is running, you can query the container's prediction endpoint to get predictions from your Language Understanding model. This endpoint can be accessed using the API endpoint URL provided by the container.
Step 6: Import the endpoint logs for active learning If you want to enable active learning, you can import the endpoint logs into the LUIS portal. This will allow you to review user queries and improve the accuracy of your Language Understanding model.
Step 7: Log user queries to enable active learning To enable active learning, you need to log user queries in the container. This can be done using the container's logging functionality. By logging user queries, you can identify areas where your Language Understanding model needs improvement and refine it over time.
In summary, to use the LUIS container, you need to download the container image, export your Language Understanding model from the LUIS portal, move the application package to the required input directory, run the container with the required input mount and billing settings, query the container's prediction endpoint, import endpoint logs for active learning, and log user queries to enable active learning.
