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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.

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Customized model monitoring for near real-time batch inference with Amazon SageMaker

AWS Machine Learning

Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. A preprocessor script is a capability of SageMaker Model Monitor to preprocess SageMaker endpoint data capture before creating metrics for model quality.

Scripts 109
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. This approach was not only time-consuming but also prone to errors and difficult to scale.

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20 Business Leaders Share How Call Centers Can Address Increased Customer Vulnerability

Callminer

Firstly, contact centers can make use of call analytics software to analyze past call recordings and use them to train agents how to identify vulnerable customers. Older citizens, the unhealthy, and those in low-income areas have always been targets for social engineering. You can also inform them of their increased vulnerability.

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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

We demonstrate how two different personas, a data scientist and an MLOps engineer, can collaborate to lift and shift hundreds of legacy models. SageMaker runs the legacy script inside a processing container. We assume the involvement of two personas: a data scientist and an MLOps engineer.

Scripts 142
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Audio Capture Powers Automated QA & Real-Time Agent Guidance

OrecX

Some of those ways include: Automated QA - Organizations can now capture and automatically analyze (with the addition of AI and speech analytics ) all their agent calls rather than a mere sampling, without adding more supervisors and quality evaluators. Supervisors can then intervene live to stave off any issues.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

This requirement translates into time and effort investment of trained personnel, who could be support engineers or other technical staff, to review tens of thousands of support cases to arrive at an even distribution of 3,000 per category. Sonnet prediction accuracy through prompt engineering. We expect to release version 4.2.2