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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. Solution overview The solution outlines how to build a reverse image search engine to retrieve similar images based on input image queries. Engine : Select nmslib. Distance metric : Select Euclidean.

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20 Call Center Pros Share the Most Undervalued Call Center Metrics and How To Better Leverage Them

Callminer

From essentials like average handle time to broader metrics such as call center service levels , there are dozens of metrics that call center leaders and QA teams must stay on top of, and they all provide visibility into some aspect of performance. Kaye Chapman @kayejchapman. First contact resolution (FCR) measures might be…”.

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

AWS Machine Learning

Investors and analysts closely watch key metrics like revenue growth, earnings per share, margins, cash flow, and projections to assess performance against peers and industry trends. Draft a comprehensive earnings call script that covers the key financial metrics, business highlights, and future outlook for the given quarter.

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

This approach allows organizations to assess their AI models effectiveness using pre-defined metrics, making sure that the technology aligns with their specific needs and objectives. Curated judge models : Amazon Bedrock provides pre-selected, high-quality evaluation models with optimized prompt engineering for accurate assessments.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

By documenting the specific model versions, fine-tuning parameters, and prompt engineering techniques employed, teams can better understand the factors contributing to their AI systems performance. Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified.

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Contact Center Metrics That Matter

Speaker: Nate Brown, Co-Founder, CX Accelerator

Do you have the right metrics in place to assess your true impact? Learn how to round out your CX dashboard with metrics related to the employee experience, the customer journey, and business results. How to move beyond effort using Customer Engagement Score, Customer Growth Engine and more to develop a CX dashboard.

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The Challenges of Omnichannel: Why so Many Contact Centers Struggle with Digital Self-Service

To find how contact centers are navigating the transition to omnichannel customer service, Calabrio surveyed more than 1,000 marketing and customer experience leaders in the U.S. about their digital customer communication strategies. Read the report to find out what was uncovered.