Automated deployment - Media2Cloud on AWS

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 on AWS solution into your account.

Time to deploy: Approximately 25 minutes

Deployment overview

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 ingestion and analysis workflows.

Step 3. Create your face collection

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

Step 4: Advanced search

  • Find the specific moment you are looking for.

Step 5: Customizing AI/ML settings

  • Configure the AI/ML services that you want to use in your analysis.

Step 6: Viewing statistics

  • A summary of all content in your collection.

Step 1. Launch the stack

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


You are responsible for the cost of the AWS services used while running this solution. For more details, visit the Cost section in this guide, and refer to the pricing webpage for each AWS service used in this solution.

  1. Sign in to the AWS Management Console and select the button to launch the Media2Cloud on AWS CloudFormation template.

                Media2Cloud on AWS launch button

    Alternatively, you can download the template as a starting point for your own implementation.

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

  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 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 SNS topic. Subscribed users will receive ingestion, 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 classes 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, refer to Choosing the price class.

    Amazon OpenSearch Service Cluster Size

    Development and Testing

    A drop-down box with four Amazon OpenSearch Service cluster sizes: Development and Testing, Suitable for Production Workloads, Recommended for Production Workloads, and Recommended for Large Production Workloads.

    Analysis Feature(s)


    A drop-down box with nine presets: Default, All, Video analysis, Audio analysis, Image analysis, Document analysis, Celebrity recognition only, Video segment detection only, and Speech to text only. For more information about the presets, refer to Analysis workflow.

    (Optional) User Defined Amazon S3 Bucket for ingest <Requires Input> If you have an existing bucket that you would like to store uploaded contents, specify the bucket name. Otherwise, leave it blank to auto create a new bucket.
    (Optional) Allow autostart on ingest S3 bucket


    A drop-down box to specify if you would like to automatically start workflow when directly upload assets to Amazon S3 ingestion bucket.
  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 boxes acknowledging that the template will create 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 receive a CREATE_COMPLETE status 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 ingestion, 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 Upload tab.

  5. Drag and drop your files to the Upload Video box, or choose the Browse Files button to upload a video or image file. Once the files are uploaded, choose Quick upload, or select Next to Start Upload.

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

Step 3. 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 select FaceCollection in the top navigation.

  2. Type in the name of the face collection in the blank field and choose Create New Collection.

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

  4. Choose the Play button again and then choose Pause once you find a face in the content.

  5. Move the toggle by Snapshot Mode to the right to display a bounding box.

  6. Adjust the size of the bounding to fit tightly over the face.

  7. Type the name of the person in the Name box and select your Face Collection from the dropdown menu.

  8. Once finished, choose the Index Face button.

  9. Repeat steps 4-8 until you have identified all of the faces.

  10. 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.

Included in the web interface is the ability to search for specific moments across the analyzed content. A user has the ability to put in specific search terms and have timestamped results returned.

  1. In the web interface select Collection in the top navigation bar.

  2. On the collection page, there is a search bar in the top right-hand corner of the page. Deselect any of the attributes that you want excluded from your search and then type a term or phrase in the Search box and hit submit.

  3. Assets matching the search term will be presented under the Search Results section of the page and highlight where there was a match to your search term.

  4. Choose the file thumbnail in the search results to be taken to that asset.

Step 5. Customizing AI/ML settings

In this version of Media2Cloud on AWS, users have a lot of flexibility on the AI/ML services that are used. They also have the ability to configure those services for their use cases.

  1. In the web interface select Settings from the top navigation bar.

  2. In the Amazon Rekognition Settings section:

    • You can set the minimum confidence level that you want results from.

    • Toggle on or off specific detection types.

    • Select the face collection that you want to use when analyzing assets.

    • When using Amazon Rekognition to detect text on screen, you can select the specific regions of the screen for analysis.

    • If you have created a custom AI/ML model using Amazon Rekognition Custom Labels, you can use that model when analyzing assets.

    • The Frame Based Analysis section give the flexibility to switch from the Amazon Rekognition Video API to the Amazon Rekognition Image API. When you toggle the Frame Based Analysis button on, you can determine the frequency that frames are analyzed.

  3. In the Amazon Transcribe settings section:

    • Select the language that you want Amazon Transcribe to create a transcript of the video in. For a complete list of supported languages, refer to Amazon Transcribe Supported Languages.

    • If you have created a Custom Vocabulary to improve the accuracy of Amazon Transcribe, you can select that model for the analysis of your assets.

    • If you have created a Custom Language Model you can activate that model for the analysis of your assets.

  4. In the Amazon Comprehend settings section:

    • Activate Entity Detection, Sentiment Analysis, and Key phrase Detection.

    • If you have built a Custom Entity Recognizer to identify custom entities for your business needs, you can activate that as well.

  5. In the Amazon Textract settings section, you can activate the service to extract text from documents that you are analyzing.

Step 6. Viewing statistics

Once content has been analyzed, the web interface has a way to show an aggregation of the metadata generated by the AI/ML Services. This helps to answer the question of what the most popular or frequent tags and detections are in the library.

  1. In the web interface select Stats from the top navigation bar.

  2. Pie charts show the overall and categorized statistics of your content collection.