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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. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.
They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. Similarly, you can explore image and video models with the Image & video playground. Download all three sample data files.
In a recent survey, 79% of consumers stated they rely on user videos, comments, and reviews more than ever and 78% of them said that brands are responsible for moderating such content. Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged.
In this post, we demonstrate how to use enhanced video search capabilities by enabling semantic retrieval of videos based on text queries. Overall, we aim to improve video search through cutting-edge semantic matching, providing an efficient way to find videos relevant to your rich textual queries.
The Amazon Nova family of models includes Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro, which support text, image, and video inputs while generating text-based outputs. How do Amazon Nova Micro and Amazon Nova Lite perform against GPT-4o mini in these same metrics? get("message", {}).get("content")
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required.
A seamless search journey not only enhances the overall user experience, but also directly impacts key business metrics such as conversion rates, average order value, and customer loyalty. This helps the search be more resilient to phrasing variations and to accept multimodal inputs such as text, image, audio, and video.
In this post, we discuss the key elements needed to evaluate the performance aspect of a content moderation service in terms of various accuracy metrics, and a provide an example using Amazon Rekognition Content Moderation API’s. Measure model accuracy on videos. What to evaluate. Measure model accuracy on images.
Video generation has become the latest frontier in AI research, following the success of text-to-image models. This text-to-videoAPI generates high-quality, realistic videos quickly from text and images. Luma AI’s recently launched Dream Machine represents a significant advancement in this field.
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. Distance metric : Select Euclidean. Vector field name : Enter a name, such as vector. Engine : Select nmslib.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.
In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos. Users can input audio, video, or text into GenASL, which generates an ASL avatar video that interprets the provided data.
Gain insights into training strategies, productivity metrics, and real-world use cases to empower your developers to harness the full potential of this game-changing technology. Discover how to create and manage evaluation jobs, use automatic and human reviews, and analyze critical metrics like accuracy, robustness, and toxicity.
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. Also note the completion metrics on the left pane, displaying latency, input/output tokens, and quality scores.
The integration offers enterprise-grade features including model evaluation metrics, fine-tuning and customization capabilities, and collaboration tools, all while giving customers full control of their deployment. Customers can use G6e instances to deploy LLMs and diffusion models for generating images, video, and audio.
In today’s digital landscape, the demand for audio and video content is skyrocketing. From product documentation in video format to podcasts replacing traditional blog posts, content creators are exploring diverse channels to reach a wider audience. The response from API calls are displayed to the end-user.
You train the model using semi-structured documents, which includes the following document types such as digital and scanned PDF documents and Word documents; Images sunch as JPG files, PNG files, and single-page TIFF files and Amazon Textract API output JSON files. The model detects the input email text is a non-phishing email.
The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Amazon Rekognition offers pre-trained and customizable computer vision (CV) capabilities to extract information and insights from your images and videos.
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.
As you’ll see further in the post, Verisk incorporated the Retrieve API and the Query API to retrieve semantically relevant passages for their queries to further improve generation by the LLM. Preprocessing of Images and Videos – The outputs from Amazon Rekognition and Amazon Transcribe were fed into Claude.
Solution overview Knowledge Bases for Amazon Bedrock allows you to configure your RAG applications to query your knowledge base using the RetrieveAndGenerate API , generating responses from the retrieved information. An example query could be, “What are the recent performance metrics for our high-net-worth clients?”
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.
The AI Service Layer allows Domo to switch between different models provided by Amazon Bedrock for individual tasks and track their performance across key metrics like accuracy, latency, and cost. The following video of Domo.AI provides a more detailed overview of the product’s key features and capabilities.
However, bad actors increasingly deploy spoof attacks using the user’s face images or videos posted publicly, captured secretly, or created synthetically to gain unauthorized access to the user’s account. At the end of the countdown, a video recording begins, and an oval appears on the screen.
Earlier this year, we announced Amazon Bedrock , a serverless API to access foundation models from Amazon and our generative AI partners. Although it’s currently in Private Preview, its serverless API allows you to use foundation models from Amazon, Anthropic, Stability AI, and AI21, without having to deploy any endpoints yourself.
Amazon Kendra is an intelligent search service powered by ML, and Amazon Rekognition is an ML service that can identify objects, people, text, scenes, and activities from images or videos. The first aspect is performed by Amazon Rekognition, which can identify objects, people, text, scenes, and activities from images or videos.
It evaluates each user query to determine the appropriate course of action, whether refusing to answer off-topic queries, tapping into the LLM, or invoking APIs and data sources such as the vector database. For instance, if the question is related to audience forecasting, the agent will invoke Amazon internal Audience Forecasting API.
Businesses can track key metrics related to agent performance, customer satisfaction, and operational efficiency across all channels. API and Integrations: WFO solutions arent the only thing your contact center software should connect with. Genesys Cloud is known for its open API and extensive customization options.
Eventually, your metrics will evolve from productivity to production, as your business evolves the contact center into a profit center, proactively resolving customer issues and offering clients bespoke products and services to prevent future mishaps. Prioritize your clients’ concerns.
To solve this problem, we propose the use of generative AI, a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. This metric compares an automatically produced summary against a reference or a set of references (human-produced) summary or translation.
WebRTC is an open source project that gives developers the tools to embed live communications streams like voice, video and chat directly into any web page, with relative ease. WebRTC provides developers with application programming interfaces (APIs) using Javascript, which allows for much more straightforward and speedier development.
For a high-level overview of how MME work, check out the AWS Summit video Scaling ML to the next level: Hosting thousands of models on SageMaker. It provides a REST API with a web server to serve and manage multiple models on a single host. Key metrics to monitor your endpoint performance. Platform-level metrics (MME metrics).
Amazon Rekognition is a computer vision service that makes it simple to add image and video analysis to your applications using proven, highly scalable, deep learning technology that does not require machine learning (ML) expertise. The Evaluation tab shows metrics for each label, and the average metric for the entire test dataset.
As new data is provided, Forecast automatically computes predictor accuracy metrics on the new dataset, providing you with more information to decide whether to keep using, retrain, or create new predictors. You can enable this feature with one click on the AWS Management Console or using Forecast APIs. Predictor accuracy over time.
This module implements an observability solution, including application logs, application performance monitoring (APM), and metrics. The Very Group engineering squads are now using the service directly through an API endpoint to put logs straight into Elasticsearch. The adoption of Logstash was initially done seamlessly.
Amazon Comprehend provides customized features, custom entity recognition , custom classification , and pre-trained APIs such as key phrase extraction, sentiment analysis, entity recognition, and more so you can easily integrate NLP into your applications. JumpStart also provides other resources like notebooks, blogs, and videos.
In the modern world of instant messaging, fast broadband speeds, and direct video conferencing, the last thing anyone wants is to wait. To sum it up, the Spearline PDD test is a “call answer time” metric that includes any delay added by intermediary networks. Nobody likes delays.
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.
However, most summaries are empty or inaccurate because manually creating them is time-consuming, impacting agents’ key metrics like average handle time (AHT). The following video shows an example of the Live Call Analytics with Agent Assist summarizing an in-progress call, summarizing after the call ends, and generating a follow-up email.
With a multitude of articles, videos, audio recordings, and other media created daily across news media companies, readers of all types—individual consumers, corporate subscribers, and more—often find it difficult to find news content that is most relevant to them.
In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.
After specifying the metrics that you want to track, you can identify which campaigns and recommenders are most impactful and understand the impact of recommendations on your business metrics. All customers want to track the metric that is most important for their business.
Amazon EKS creates a highly available endpoint for the managed Kubernetes API server that you use to communicate with your cluster (using tools like kubectl). The managed endpoint uses Network Load Balancer to load balance Kubernetes API servers. In his free time, he enjoys playing video games, reading books, and writing software.
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