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Reducing risk with speech analytics: Five easy things you can do right away

LiveVox

Speech analytics is no longer a new thing. Having come on to the scene in around 2012, the varied use cases have since been well-documented. The post Reducing risk with speech analytics: Five easy things you can do right away appeared first on Livevox.

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Close to You

Contact Center Pipeline

In 2012, Rolling Stone […]. The album contained the hit song and perennial wedding favorite, “We’ve Only Just Begun,” and the song that provided the Carpenters with international musical fame, “(They Long to Be) Close to You.”

Analytics 279
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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

By using the Livy REST APIs , SageMaker Studio users can also extend their interactive analytics workflows beyond just notebook-based scenarios, enabling a more comprehensive and streamlined data science experience within the Amazon SageMaker ecosystem. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*" elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

Big data 115
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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

He helps customers implement generative AI, machine learning, and analytics solutions. Try out the code examples in this post to implement your own RAG solution using Amazon Bedrock Knowledge Bases, and share your feedback and questions in the comments section.

APIs 102
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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

With a background in AI/ML, data science, and analytics, Yunfei helps customers adopt AWS services to deliver business results. He designs AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. About the authors Yunfei Bai is a Senior Solutions Architect at AWS.

APIs 93
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Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning

He uses his expertise in artificial intelligence and advanced analytics to extract valuable insights and drive meaningful business outcomes for customers. He also has experience with building end-to-end MLOps pipelines to productionize analytical models. In his free time, Matteo enjoys traveling and reading.

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How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances

AWS Machine Learning

Create a role named sm-build-role with the following trust policy, and add the policy sm-build-policy that you created earlier: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "codebuild.amazonaws.com" }, "Action": "sts:AssumeRole" } ] } Now, let’s review the steps in CloudShell. Dima has a M.Sc

APIs 136