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Large language models (LLMs) have remarkable capabilities. Nevertheless, using them in customer-facing applications often requires tailoring their responses to align with your organization’s values and brand identity. In this post, we demonstrate how to use direct preference optimization (DPO), a technique that allows you to fine-tune an LLM with human preference data, together with Amazon …

Align Meta Llama 3 to human preferences with DPO, Amazon SageMaker Studio, and Amazon SageMaker Ground Truth Read More »

State-of-the-art generative AI models and high performance computing (HPC) applications are driving the need for unprecedented levels of compute. Customers are pushing the boundaries of these technologies to bring higher fidelity products and experiences to market across industries. The size of large language models (LLMs), as measured by the number of parameters, has grown exponentially …

Amazon EC2 P5e instances are generally available Read More »

This post is co-written with Joe Clark from Domo. Data insights are crucial for businesses to enable data-driven decisions, identify trends, and optimize operations. Traditionally, gaining these insights required skilled analysts using specialized tools, which can make the process slow and less accessible. Generative artificial intelligence (AI) has revolutionized this by allowing users to interact …

Exploring data using AI chat at Domo with Amazon Bedrock Read More »

This post was co-written with Mickey Alon from Vidmob. Generative artificial intelligence (AI) can be vital for marketing because it enables the creation of personalized content and optimizes ad targeting with predictive analytics. Specifically, such data analysis can result in predicting trends and public sentiment while also personalizing customer journeys, ultimately leading to more effective …

How Vidmob is using generative AI to transform its creative data landscape Read More »

Generative artificial intelligence (AI) models have become increasingly popular and powerful, enabling a wide range of applications such as text generation, summarization, question answering, and code generation. However, despite their impressive capabilities, these models often struggle with domain-specific tasks or use cases due to their general training data. To address this challenge, fine-tuning these models …

Fine-tune Llama 3 for text generation on Amazon SageMaker JumpStart Read More »

Generative artificial intelligence (AI) applications powered by large language models (LLMs) are rapidly gaining traction for question answering use cases. From internal knowledge bases for customer support to external conversational AI assistants, these applications use LLMs to provide human-like responses to natural language queries. However, building and deploying such assistants with responsible AI best practices …

Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval Read More »

This post was co-written with Jerry Liu from LlamaIndex. Retrieval Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models (LLMs). By combining the vast knowledge stored in external data sources with the generative power of LLMs, RAG enables you to tackle complex tasks that require both knowledge …

Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock Read More »

As generative artificial intelligence (AI) continues to revolutionize every industry, the importance of effective prompt optimization through prompt engineering techniques has become key to efficiently balancing the quality of outputs, response time, and costs. Prompt engineering refers to the practice of crafting and optimizing inputs to the models by selecting appropriate words, phrases, sentences, punctuation, …

Evaluating prompts at scale with Prompt Management and Prompt Flows for Amazon Bedrock Read More »

Kubernetes is a popular orchestration platform for managing containers. Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the …

Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes Read More »

Generative artificial intelligence (AI) foundation models (FMs) are gaining popularity with businesses due to their versatility and potential to address a variety of use cases. The true value of FMs is realized when they are adapted for domain specific data. Managing these models across the business and model lifecycle can introduce complexity. As FMs are …

Effectively manage foundation models for generative AI applications with Amazon SageMaker Model Registry Read More »