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Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Building generative AI applications requires more than model API calls.
All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers. AWS Lambda is an event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.
During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.
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. The introduction of an LLM-as-a-judge framework represents a significant step forward in simplifying and streamlining the model evaluation process.
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.
The implementation uses Slacks event subscription API to process incoming messages and Slacks Web API to send responses. The incoming event from Slack is sent to an endpoint in API Gateway, and Slack expects a response in less than 3 seconds, otherwise the request fails.
The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. EventBridge monitors status change events to automatically take actions with simple rules. API Gateway invokes a Lambda function to initiate model updates.
Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged. Some customers have asked if they could use this approach to moderate videos by sampling image frames and sending them to the Amazon Rekognition image moderation API.
It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral 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.
Using software to detect a sound is called audio event detection , and it has a number of applications. In a healthcare environment, you can use audio event detection to passively listen for sounds from a patient that indicate an acute health problem. Using audio data with machine learning. About the authors.
The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. 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.
Deploy the API to make predictions. Select options and train the model The next step towards building and training a fraud detector model is to define the business activity (event) to evaluate for the fraud. Under Event type details , enter docfraud as the event type name and, optionally, enter a description of the event.
The GenASL web app invokes the backend services by sending the S3 object key in the payload to an API hosted on Amazon API Gateway. API Gateway instantiates an AWS Step Functions The state machine orchestrates the AI/ML services Amazon Transcribe and Amazon Bedrock and the NoSQL data store Amazon DynamoDB using AWS Lambda functions.
AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards. A user sends a question (NLQ) as a JSON event. If it finds any, it skips to Step 6.
Forecasting Core Features The Ability to Consume Historical Data Whether it’s from a copy/paste of a spreadsheet or an API connection, your WFM platform must have the ability to consume historical data. However, it’s helpful to capture those events and store them for future use.
The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.
This requires carefully combining applications and metrics to provide complete awareness, accuracy, and control. The zAdviser uses Amazon Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data. It’s also vital to avoid focusing on irrelevant metrics or excessively tracking data.
Whenever drift is detected, an event is launched to notify the respective teams to take action or initiate model retraining. Event-driven architecture – The pipelines for model training, model deployment, and model monitoring are well integrated by use Amazon EventBridge , a serverless event bus.
Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. You can then use your custom model via the Rekognition Custom Labels API and integrate it into your applications.
Every ‘event’ that happens online can be gold dust for marketers. The greater the visibility you have of the data needed to track conversion events, optimise ads and re-target users, the stronger the position you are in as a marketer. Accurately tracking and improving campaign performance is at the top of every marketer’s wish list.
A Lambda function called the Call Event Processor, fed by Kinesis Data Streams, processes and optionally enriches meeting metadata and transcription segments. The Call Event Processor integrates with the meeting assist services. The stacks take about 35–40 minutes to deploy. Authentication is provided by Amazon Cognito.
It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab. The F1 score provides a balanced evaluation of the model’s performance.
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. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.
Company earnings calls are crucial events that provide transparency into a company’s financial health and prospects. 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.
You can use Amazon Comprehend to identify the language of the text; extract key phrases, places, people, brands, or events; understand sentiment about products or services; and identify the main topics from a library of documents. You can train a custom classifier using either the Amazon Comprehend console or API.
By uploading a small set of training images, Amazon Rekognition automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. For more details on the different metrics such as precision, recall, and F1, refer to Metrics for evaluation your model.
Another option is to use an unsupervised anomaly detection model to monitor and surface unusual behavior among customer events. The challenge with this approach is that not all fraud events are anomalies, and not all anomalies are indeed fraud. The following table defines these metrics. AUC uncertainty interval > 0.3
Therefore, preventing mobile fraud starts from clearly understanding the risk profile of the devices used to access the mobile app and then using the device risk intelligence gathered, together with additional data about the user, event, or account, to detect potential fraudulent behavior in real time and at scale.
Success Metrics for the Team. Ultimately, the biggest success metric for the Champion is to be able to show the Executive Sponsor and key Stakeholders that real business value has been gained through the use of customer journey analytics. Success Metrics for the Project. Success Metrics for the Business. Churn Rate.
Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.
Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.
Autopilot training jobs start their own dedicated SageMaker backend processes, and dedicated SageMaker API calls are required to start new training jobs, monitor training job statuses, and invoke trained Autopilot models. We use a Lambda step because the API call to Autopilot is lightweight. script creates an Autopilot job.
Amazon CloudWatch – A monitoring and observability service that collects monitoring and operational data in the form of logs, metrics, and events. Images that are captured through the motion sensor camera traps are uploaded to an S3 bucket, which publishes an event for every uploaded image. Lambda function code. sharp – ^0.30.7:
Imperva Cloud WAF protects hundreds of thousands of websites against cyber threats and blocks billions of security events every day. Counters and insights based on security events are calculated daily and used by users from multiple departments. The idea is to use metrics to compare experiments during development.
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 Fraud Detector relies on specific models with tailored algorithms, enrichments, and feature transformations to detect fraudulent events across multiple use cases. The ATI model is trained using a dataset containing your business’s historical login events. Define the entity, event and event variables, and event label (optional).
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. Happy days! You can check framerate information for video here too.
The Totango API provides a full customer 360 view and helps improve your team’s efficiency so you can take your customer success to the next level. . There are apps for email, taking notes, tracking calendar events, posting to social media and so much more. That’s where workflow automation tools can help.
The response from API calls are displayed to the end-user. The Mediasearch solution has an event-driven serverless computing architecture with the following steps: You provide an S3 bucket containing the audio and video files you want to index and search. This is also known as the MediaBucket.
Solution overview In this section, we present a generic architecture that is similar to the one we use for our own workloads, which allows elastic deployment of models using efficient auto scaling based on custom metrics. The reverse proxy collects metrics about calls to the service and exposes them via a standard metricsAPI to Prometheus.
If it detects error messages specifically related to the Neuron device (which is the Trainium or AWS Inferentia chip), it will change NodeCondition to NeuronHasError on the Kubernetes API server. The node recovery agent is a separate component that periodically checks the Prometheus metrics exposed by the node problem detector.
SageMaker services, such as Processing, Training, and Hosting, collect metrics and logs from the running instances and push them to users’ Amazon CloudWatch accounts. SageMaker has native integration with the Amazon EventBridge , which monitors status change events in SageMaker. Analyzing log data with CloudWatch Log Insights queries.
The LAYOUT feature of AnalyzeDocument API can now detect up to ten different layout elements in a document’s page. It is important to note that layout elements appear in the correct reading order in the API response as the reading order in the document, which makes it easy to construct the layout text from the API’s JSON response.
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