The second of the series is about Azure AI containers.

Azure AI containers allow you to run Azure AI services on-premises or in any environment that supports Docker. This provides flexibility to bring Azure AI capabilities closer to your data for compliance, security, or operational reasons. Here’s how you can use them:

What Are Azure AI Containers?

Azure AI containers package Azure AI services into Docker containers, enabling you to deploy and run these services locally or in your preferred environment. This approach is beneficial for scenarios where data cannot be sent to the cloud due to compliance or security requirements 1.

Benefits of Azure AI Containers

  • Control Over Data: Process data locally without sending it to the cloud.
  • Consistency: Maintain consistent environments across on-premises, cloud, and edge deployments.
  • Scalability: Scale services to meet high throughput and low latency requirements.
  • Flexibility: Update and version models as needed.

How to Use Azure AI Containers

Step 1: Pull the Container Image

First, pull the desired Azure AI container image from the Microsoft Container Registry. For example, to pull the Text Analytics container:

docker pull mcr.microsoft.com/azure-cognitive-services/textanalytics:latest

Step 2: Run the Container

Run the container using Docker. You can specify environment variables and mount volumes as needed:

docker run -d -p 5000:5000 --name textanalytics \
  -e EULA=accept \
  -e ApiKey=<your_api_key> \
  mcr.microsoft.com/azure-cognitive-services/textanalytics:latest

Step 3: Configure Networking

Ensure your container is accessible within your network. You might need to configure firewall rules or use a reverse proxy like Traefik to manage traffic.

Step 4: Access the Service

Once the container is running, you can access the service via the exposed port. For example, you can send requests to http://localhost:5000/text/analytics/v3.0/entities.

Step 5: Monitor and Scale

Use tools like Prometheus and Grafana to monitor the performance and health of your containers. You can scale your containers based on demand using Docker Compose or Kubernetes.

Example Docker Compose Configuration

Here’s an example docker-compose.yml file to run multiple Azure AI containers:

version: '3'

services:
  textanalytics:
    image: mcr.microsoft.com/azure-cognitive-services/textanalytics:latest
    environment:
      - EULA=accept
      - ApiKey=<your_api_key>
    ports:
      - "5000:5000"

  computervision:
    image: mcr.microsoft.com/azure-cognitive-services/computervision:latest
    environment:
      - EULA=accept
      - ApiKey=<your_api_key>
    ports:
      - "5001:5001"

Additional Resources

For more detailed instructions and a list of available Azure AI containers, you can refer to the Azure documentation on container support1.

This should be flexible and secure!

1: Azure AI containers - Azure AI services

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