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Real-time analysis of customer sentiment using AWS

AWS Machine Learning

For example, marketing could use this data to create campaigns targeting different customer segments. Traditionally, this data is collected via a batch process and sent to a data warehouse for storage, analysis, and reporting, and is made available to decision-makers after several hours, if not days. The pasta was delicious.

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5 Capabilities of Business Intelligence for Social Media Monitoring and Analytics

CSM Magazine

In this article, we’ll explore five key capabilities of BI that empower businesses to monitor social media conversations, analyze sentiment, conduct competitor analysis, create customized dashboards and reports, and integrate social media data with other sources for comprehensive analytics.

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

AWS Machine Learning

For example, Rekognition Custom Labels can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect animated characters in videos. You can also use precision or recall as your model evaluation metrics.

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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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Metrics for evaluating an identity verification solution

AWS Machine Learning

Then we dive into the two key metrics used to evaluate a biometric system’s accuracy: the false match rate (also known as false acceptance rate) and false non-match rate (also known as false rejection rate). Consider the example of Julie, a new user opening a digital bank account. The following diagram outlines the process.

Metrics 69
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step. RMSE threshold: This step verifies the trained model metric (RMSE) against a defined threshold to decide whether to proceed towards endpoint deployment or reject this model.

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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.,

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