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Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.
Organizations across media and entertainment, advertising, social media, education, and other sectors require efficient solutions to extract information from videos and apply flexible evaluations based on their policies. Popular use cases Advertising tech companies own video content like ad creatives.
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In the context of generative AI , significant progress has been made in developing multimodal embedding models that can embed various data modalities—such as text, image, video, and audio data—into a shared vector space. In this post, you will use OpenSearch Serverless as the vector database for the image embeddings.
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So, this week, we put together some video you can queue up and watch. This video is mostly about their WebEx plans, with a bit of call center content, but her energy is really infectious; it’s a joy to watch. Enterprise Connect included a panel featuring: Sheila McGee-Smith, President and Principal Analyst, McGee-Smith Analytics.
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Use hybrid search and semantic search options via SDK When you call the Retrieve API, Knowledge Bases for Amazon Bedrock selects the right search strategy for you to give you most relevant results. You have the option to override it to use either hybrid or semantic search in the API.
The Amazon Bedrock API returns the output Q&A JSON file to the Lambda function. The container image sends the REST API request to Amazon API Gateway (using the GET method). API Gateway communicates with the TakeExamFn Lambda function as a proxy. The JSON file is returned to API Gateway. Successfully created!
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The translation playground could be adapted into a scalable serverless solution as represented by the following diagram using AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon API Gateway. He is passionate about enabling organizations to transform transform their business, using advanced analytics and AI.
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For example, this could be a softphone (such as Google Voice ), another meeting app, or for demo purposes, you can simply play a local audio recording or a YouTube video in your browser to emulate another meeting participant. If you just want to try it, open the following YouTube video in a new tab. For Meeting ID , enter a meeting ID.
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Define – Business Model Innovation / Industry Transformation, Machine 2 Machine; Augmented Decisions / Self Learning Analytics; Inventory Digital Assets; New Growth and Constant Innovation. Cybersecurity Readiness Assessment. To stay competitive, financial institutions are increasingly adopting cloud, mobility and the latest technologies.
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Consequently, no other testing solution can provide the range and depth of testing metrics and analytics. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. You can check framerate information for video here too. Happy days!
We have exciting new launches today including 8x8 Speech Analytics – automatically providing sentiment analysis on 100% of your customer calls; enhanced Integration framework – making it easy to embed communications into your business processes. Speech Analytics for Contact Centers. Analytics Features. Description.
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Analytics and real-time reporting. Reporting/Analytics. Analytics & Reporting. CSML helps developers build and deploy chatbots easily with its expressive syntax and its capacity to connect to any third party API. Chatbot activity analytics. Knowledge-base integration. Omni-channel (email, chat, voice, social).
A suite of purpose-built applications that provide out-of-the-box SaaS functionality for function-specific interactions, including a full cloud contact center solution, management and automation of marketing campaigns, service and operational notifications for customers and video assistance for teams and customers. What’s Next?
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Each row in this dataset contains the title of a video, its URL, and the corresponding text transcript. The question from the user is based on transcripts of videos from a YouTube channel. Video-title: $title_1, transcript-segment: $segment_1 2. Video-title: $title_1, transcript-segment: $segment_1 2. join(chunk).strip()
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