AWS Machine Learning Blog
Tag: Generative AI
Accuracy evaluation framework for Amazon Q Business
Generative artificial intelligence (AI), particularly Retrieval Augmented Generation (RAG) solutions, are rapidly demonstrating their vast potential to revolutionize enterprise operations. RAG models combine the strengths of information retrieval systems with advanced natural language generation, enabling more contextually accurate and informative outputs. From automating customer interactions to optimizing backend operation processes, these technologies are not just […]
How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock
As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.
Inference AudioCraft MusicGen models using Amazon SageMaker
Music generation models have emerged as powerful tools that transform natural language text into musical compositions. Originating from advancements in artificial intelligence (AI) and deep learning, these models are designed to understand and translate descriptive text into coherent, aesthetically pleasing music. Their ability to democratize music production allows individuals without formal training to create high-quality […]
Faster LLMs with speculative decoding and AWS Inferentia2
In recent years, we have seen a big increase in the size of large language models (LLMs) used to solve natural language processing (NLP) tasks such as question answering and text summarization. Larger models with more parameters, which are in the order of hundreds of billions at the time of writing, tend to produce better […]
Import a fine-tuned Meta Llama 3 model for SQL query generation on Amazon Bedrock
In this post, we demonstrate the process of fine-tuning Meta Llama 3 8B on SageMaker to specialize it in the generation of SQL queries (text-to-SQL). Meta Llama 3 8B is a relatively small model that offers a balance between performance and resource efficiency. AWS customers have explored fine-tuning Meta Llama 3 8B for the generation of SQL queries—especially when using non-standard SQL dialects—and have requested methods to import their customized models into Amazon Bedrock to benefit from the managed infrastructure and security that Amazon Bedrock provides when serving those models.
Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock
As generative artificial intelligence (AI) applications become more prevalent, maintaining responsible AI principles becomes essential. Without proper safeguards, large language models (LLMs) can potentially generate harmful, biased, or inappropriate content, posing risks to individuals and organizations. Applying guardrails helps mitigate these risks by enforcing policies and guidelines that align with ethical principles and legal requirements.Amazon […]
Monks boosts processing speed by four times for real-time diffusion AI image generation using Amazon SageMaker and AWS Inferentia2
This post is co-written with Benjamin Moody from Monks. Monks is the global, purely digital, unitary operating brand of S4Capital plc. With a legacy of innovation and specialized expertise, Monks combines an extraordinary range of global marketing and technology services to accelerate business possibilities and redefine how brands and businesses interact with the world. Its […]
Find answers accurately and quickly using Amazon Q Business with the SharePoint Online connector
Amazon Q Business is a fully managed, generative artificial intelligence (AI)-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise […]
Evaluate conversational AI agents with Amazon Bedrock
As conversational artificial intelligence (AI) agents gain traction across industries, providing reliability and consistency is crucial for delivering seamless and trustworthy user experiences. However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that […]
Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker
This post demonstrates how to use Amazon SageMaker to fine tune a Sentence Transformer embedding model and deploy it with an Amazon SageMaker Endpoint. The code from this post and more examples are available in the GitHub repo.