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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. The approver approves the model by following the link in the email to an API Gateway endpoint. API Gateway invokes a Lambda function to initiate model updates.

APIs 100
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Q&A recap: crash course in Customer Success and SaaS metrics with Dave Kellogg

ChurnZero

With so many SaaS metrics floating around, and even more opinions on when and how to use them, it can be hard to know if you’re measuring what really matters. Leading SaaS expert, Dave Kellogg, and ChurnZero CEO, You Mon Tsang, sat down to answer all the questions you want to know about SaaS metrics like ARR, NRR, GRR, LTV, and CAC (i.e.,

SaaS 98
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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 82
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Customize Amazon Textract with business-specific documents using Custom Queries

AWS Machine Learning

You can use the adapter for inference by passing the adapter identifier as an additional parameter to the Analyze Document Queries API request. Adapters can be created via the console or programmatically via the API. You can analyze these metrics either collectively or on a per-document basis. MICR line format).

APIs 102
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The Comprehensive Guide to NICE CXone’s Latest AI-Driven Release

Expivia

With a suite of tools ranging from Enlighten Actions to Autopilot and beyond, NICE CXone is setting a new standard for what businesses can expect from their customer service technology. These updates facilitate a more efficient management of customer service teams and a more customizable user experience.

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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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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 95