Tag: Amazon Kinesis
Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock | Amazon Web Services
See CHANGELOG for latest features and fixes.
You’ve likely experienced the challenge of taking notes during a...
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Techniques and approaches for monitoring large language models on AWS | Amazon Web Services
Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis....
Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart | Amazon Web Services
One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of...
REPLY: Storm Reply ayuda a EUROGATE Group a analizar datos de máquinas y sistemas con el acelerador Storm Innovator
TURÍN, Italia–(BUSINESS WIRE)–Storm Reply, especialista en servicios profesionales de computación en la nube, ha desarrollado una innovadora solución de IoT en la nube para...
Ball position tracking in the cloud with the PGA TOUR | Amazon Web Services
The PGA TOUR continues to enhance the golf experience with real-time data that brings fans closer to the game. To deliver even richer experiences,...
Machine Learning with MATLAB and Amazon SageMaker | Amazon Web Services
This post is written in collaboration with Brad Duncan, Rachel Johnson and Richard Alcock from MathWorks. MATLAB is a popular programming tool for a...
Implement real-time personalized recommendations using Amazon Personalize | Amazon Web Services
At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to...
Use generative AI to increase agent productivity through automated call summarization | Amazon Web Services
Your contact center serves as the vital link between your business and your customers. Every call to your contact center is an opportunity to...
Optimize equipment performance with historical data, Ray, and Amazon SageMaker | Amazon Web Services
Efficient control policies enable industrial companies to increase their profitability by maximizing productivity while reducing unscheduled downtime and energy consumption. Finding optimal control policies...
Predict vehicle fleet failure probability using Amazon SageMaker Jumpstart | Amazon Web Services
Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle...
HiveMQ Announces Integration to PostgreSQL and MongoDB Databases
"With the new MongoDB extension, HiveMQ has provided clients additional flexibility to leverage our developer data platform to build modern IoT...
Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time
Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even...
Generate actionable insights for predictive maintenance management with Amazon Monitron and Amazon Kinesis
Reliability managers and technicians in industrial environments such as manufacturing production lines, warehouses, and industrial plants are keen to improve equipment health and uptime...
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