AWS vs Azure Anomaly Detector Process


Data flow

  1. Ingests data from the various stores that contain raw data to be monitored by Anomaly Detector.
  2. Aggregates, samples, and computes the raw data to generate the time series or calls the Anomaly Detector API directly if the time series is already prepared and responds with the detection results.
  3. Queue the anomaly-related metadata.
  4. The serverless app picks the message from the message queue based on the anomaly-related metadata and sends the alert about the anomaly.
  5. Stores the anomaly detection metadata.
  6. Visualize the results of the time series anomaly detection.



  1. The Orchestrator (solution owner or DevOps engineer) launches the solution in the AWS account and selects the desired options (for example, using Amazon SageMaker Registry, or providing an existing S3 bucket).
  2. The Orchestrator uploads the required assets for the target pipeline (for example, model artifact, training data, and/or custom algorithm zip file) into the Assets S3 bucket. If Amazon SageMaker Model Registry is used, the Orchestrator (or an automated pipeline) must register the model with the Model Registry.
  3. A single account AWS CodePipeline instance is provisioned by either sending an API call to the API Gateway or by committing the mlopsconfig.json file to the Git repository. Depending on the pipeline type, the Orchestrator AWS Lambda function packages the target AWS CloudFormation template and its parameters/configurations using the body of the API call or the mlops-config.json file and uses it as the source stage for the AWS CodePipeline instance
  4. The DeployPipeline stage takes the packaged CloudFormation template and its parameters/configurations and deploys the target pipeline into the same account.
  5. After the target pipeline is provisioned, users can access its functionalities. An Amazon Simple Notification Service (Amazon SNS) notification is sent to the email provided in the solution’s launch parameters.






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