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Facebook’s Conversion API – what marketers need to know

Infinity

Every ‘event’ that happens online can be gold dust for marketers. The greater the visibility you have of the data needed to track conversion events, optimise ads and re-target users, the stronger the position you are in as a marketer. Accurately tracking and improving campaign performance is at the top of every marketer’s wish list.

APIs 52
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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. EventBridge monitors status change events to automatically take actions with simple rules. API Gateway invokes a Lambda function to initiate model updates.

APIs 98
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Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3

AWS Machine Learning

Deploy the API to make predictions. Select options and train the model The next step towards building and training a fraud detector model is to define the business activity (event) to evaluate for the fraud. Under Event type details , enter docfraud as the event type name and, optionally, enter a description of the event.

APIs 107
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Detect audio events with Amazon Rekognition

AWS Machine Learning

Using software to detect a sound is called audio event detection , and it has a number of applications. In a healthcare environment, you can use audio event detection to passively listen for sounds from a patient that indicate an acute health problem. Using audio data with machine learning. About the authors.

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Automate Amazon Rekognition Custom Labels model training and deployment using AWS Step Functions

AWS Machine Learning

Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. You can then use your custom model via the Rekognition Custom Labels API and integrate it into your applications.

APIs 80
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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

A Lambda function called the Call Event Processor, fed by Kinesis Data Streams, processes and optionally enriches meeting metadata and transcription segments. The Call Event Processor integrates with the meeting assist services. The stacks take about 35–40 minutes to deploy. Authentication is provided by Amazon Cognito.

APIs 108