Automated Deployment - Media2Cloud

Automated Deployment

Before you launch the automated deployment, please review the architecture, configuration, and other considerations discussed in this guide. Follow the step-by-step instructions in this section to configure and deploy the Media2Cloud solution into your account.

Time to deploy: Approximately 25 minutes

What We'll Cover

The procedure for deploying this architecture on AWS consists of the following steps. For detailed instructions, follow the links for each step.

Step 1. Launch the Stack

  • Launch the AWS CloudFormation template into your AWS account.

  • Enter values for required parameters: Stack Name and Email Address.

  • Review the other template parameters, and adjust if necessary.

Step 2. Upload a Video or Image File

  • Upload a file using the web interface to begin the ingest workflow.

Step 3. Create and View Metadata

  • Create the metadata from the uploaded file.

Step 4. Configure the Labeling Work Team

  • Configure your labeling work team.

Step 5. Create Your Face Collection

  • Index faces to create grow your face collection to improve face analysis results.

Step 1. Launch the Stack

This automated AWS CloudFormation template deploys the Media2Cloud solution in the AWS Cloud.


You are responsible for the cost of the AWS services used while running this solution. See the Cost section for more details. For full details, see the pricing webpage for each AWS service you will be using in this solution.

  1. Sign in to the AWS Management Console and click the button below to launch the media2cloud AWS CloudFormation template.

                            Media2Cloud solution launch button

    You can also download the template as a starting point for your own implementation.

  2. The template is launched in the US East (N. Virginia) Region by default. To launch this solution in a different AWS Region, use the region selector in the console navigation bar.


    This solution uses the Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe services which are currently available in specific AWS Regions only. Therefore, you must launch this solution in an AWS Region where these services are available. For the most current service availability by Region, refer to the AWS Regional Services List.

  3. On the Create stack page, verify that the correct template URL shows in the Amazon S3 URL text box and choose Next.

  4. On the Specify stack details page, assign a name to your solution stack.

  5. Under Parameters, review the parameters for the template, and modify them as necessary.

    This solution uses the following default parameters.

    Parameter Default Description
    Email <Requires Input> Email address of the user that will be created in the Amazon Cognito identity pool and subscribed to the Amazon Simple Notification Service (Amazon SNS) topic. Subscribed users will receive ingest, analysis, labeling, and error notifications.

    After launch, two emails will be sent to this address: one with instructions for logging in to the web interface and one confirming the Amazon SNS subscription.

    Price Class Use Only U.S., Canada and Europe A dropdown box with price class for the edge location from which Amazon CloudFront serves your requests. Choose Use Only U.S., Canada and Europe; Use U.S., Canada, Europe, Asia and Africa; or Use All Edge Locations. For more information, see Choosing the Price Class.
    Elasticsearch Cluster Size Small A drop-down box with three Amazon Elasticsearch Service (Amazon ES) cluster sizes: Small, Medium, Large.
    Default Language Code en-US The default language code used by Amazon Transcribe and Amazon Comprehend to process speech to text and Neutral Language Processing (NLP) analysis
    Analysis Feature(s) Default A drop-down box with three presets: DefaultDefault, All, and Audio and Text. For more information about the presets, see Analysis Process.
    Anonymous Usage Yes Send anonymous usage data to AWS to help us understand solution usage across our customer base as a whole. To opt out to this feature, select No.

    For more information, see Appendix I

  6. Choose Next.

  7. On the Configure stack options page, choose Next.

  8. On the Review page, review and confirm the settings. Be sure to check the box acknowledging that the template will create AWS Identity and Access Management (IAM) resources.

  9. Choose Create stack to deploy the stack.

    You can view the status of the stack in the AWS CloudFormation console in the Status column. You should see a status of CREATE_COMPLETE in approximately 25 minutes.

Step 2. Upload a Video or Image File

After the solution successfully launches, you can start uploading video or image files for processing. The solution sends two emails: one with the subscription confirmation for the Amazon SNS topic to send ingest, analysis, labeling, and error notifications, and one with instructions for signing into the solution’s provided web interface.

  1. In the M2CStatus email, select Confirm subscription to subscribe to the Amazon SNS topic.

  2. In the second email, follow the instructions to sign in to the website.

    You will be prompted to change the password the first time you sign in.

  3. Choose Sign in on the upper right corner of the page and sign in using your recently created password.

  4. Navigate to the Demo tab.

  5. Choose the plus (+) sign, upload a video or image file, and choose Quick upload. On the next screen, choose Start Upload.

    Once the ingest process is completed, a thumbnail image of the video or image is created. You can hover on the thumbnail image and select Play now to view the media file.

Step 3. Create and View Metadata

  1. Hover over the created video or image proxy, right-click to access the menu options, and select Create metadata.

    Once the process is completed, you can view the metadata.

  2. Choose Play now.

  3. In the Play window, scroll to the carousal to view the archive and media information (or EXIF information for images), transcription, Amazon Rekognition, and Amazon Comprehend results.

  4. To view the metadata results for each result, select each individually.

Step 4. Configure the Labeling Work Team

The solution integrates with Amazon SageMaker Ground Truth private workforce. You can use the web interface to configure and manage a work team. You can invite or remove workers from your work team.

Use the following procedure to add a member to the work team.

  1. In the solution’s web interface, navigate to the Settings page. Under Ground Truth Workforce settings, choose Run wizard.

  2. In the wizard dialog box, choose Start.

  3. In the Choose a work team window, select the solution’s default work team and choose Next.

  4. Add or remove members.

    • To add a member, in the Manage team member window, enter the member’s email address and choose Add member.

      The labeling team member receives an invitation email with instructions to sign in to the labeling portal.

    • To remove a member, in the Manage team member window, enter the member’s email address and choose Remove member.

Step 5. Create Your Face Collection

The web interface allows you to create your own Amazon Rekognition face collection and index and store faces in the collection to improve the analysis results.

  1. In the web interface, hover over a created video or image and choose Play now.

  2. Scroll through the carousal until the option to create a Snapshot appears.

  3. To crop a face on the video or image, choose Snapshot.

  4. To index the face, choose Index now or Queue for later.

    • To index the face immediately, choose Index now. In the who is this person? field, enter the first and last name of the cropped face and choose Index now. The cropped face is indexed with the name you entered in your Amazon Rekognition face collection.

    • To create a list of faces to be sent to Amazon SageMaker Ground Truth for labeling by your labeling work team, choose Queue for later. This option temporarily stores the face in a DynamoDB table, enabling you to accumulate faces from the same video, from other videos, and from images. When you are ready to label the faces, choose Send to Ground Truth to send all the accumulated faces to the labeling workflow. The members of your work team will receive notification containing the access details to perform the labeling job.

  5. After the faces are indexed, choose Re-analyze to analyze the video or image using the newly indexed faces in your face collection so that all unidentified faces are recognized and indexed.