Integrate QnABot on AWS with ServiceNow
Do your employees wait for hours on the telephone to open an IT ticket? Do they wait for an agent to triage an issue, which sometimes only requires restarting the…
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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart
One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of reference content related to an input…
Build a movie chatbot for TV/OTT platforms using Retrieval Augmented Generation in Amazon Bedrock
In this post, we show you how to securely create a movie chatbot by implementing RAG with your own data using Knowledge Bases for Amazon Bedrock. We use the IMDb…
Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2
In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine…
Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1
With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform a range of generative tasks such…
Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart
When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a…
Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace
This post discusses how enterprises can build accurate, transparent, and secure generative AI applications while keeping full control over proprietary data. The proposed solution is a RAG pipeline using an…
Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained…
Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium
Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Using AWS Trainium and Inferentia…
Host the Whisper Model on Amazon SageMaker: exploring inference options
OpenAI Whisper is an advanced automatic speech recognition (ASR) model with an MIT license. ASR technology finds utility in transcription services, voice assistants, and enhancing accessibility for individuals with hearing…