AWSML

Organizations generate vast amounts of data that is proprietary to them, and it’s critical to get insights out of the data for better business outcomes. Generative AI and foundation models (FMs) play an important role in creating applications using an organization’s data that improve customer experiences and employee productivity. The FMs are typically pretrained on … Read more

Llama 3, Meta’s latest large language model (LLM), has taken the artificial intelligence (AI) world by storm with its impressive capabilities. As developers and businesses explore the potential of this powerful model, crafting effective prompts is key to unlocking its full potential. In this post, we dive into the best practices and techniques for prompting … Read more

In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. Many health insurance plan beneficiaries find it challenging to navigate through the complex member portals provided by their insurance plans. These portals often require multiple clicks, filters, and searches to find specific information about … Read more

As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. In what we consider “Act 1” of the generative AI story, we saw previously unimaginable amounts of data and compute create models that showcase the power of generative … Read more

Thomson Reuters, a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. The introduction of generative AI provides another opportunity for Thomson Reuters to work with customers and advance how they do their work, helping professionals draw insights and automate workflows, enabling them … Read more

We are thrilled to introduce Amazon Elastic Kubernetes Service (Amazon EKS) support in Amazon SageMaker HyperPod, a purpose-built infrastructure engineered with resilience at its core. This capability allows for the seamless addition of SageMaker HyperPod managed compute to EKS clusters, using automated node and job resiliency features for foundation model (FM) development. FMs are typically … Read more

Data contains information, and information can be used to predict future behaviors, from the buying habits of customers to securities returns. Businesses are seeking a competitive advantage by being able to use the data they hold, apply it to their unique understanding of their business domain, and then generate actionable insights from it. The financial … Read more

Time series data is a distinct category that incorporates time as a fundamental element in its structure. In a time series, data points are collected sequentially, often at regular intervals, and they typically exhibit certain patterns, such as trends, seasonal variations, or cyclical behaviors. Common examples of time series data include sales revenue, system performance … Read more

Technology operations (TechOps) refers to the set of processes and activities involved in managing and maintaining an organization’s IT infrastructure and services. There are several terminologies used with reference to managing information technology operations, including ITOps, SRE, AIOps, DevOps, and SysOps. For the context of this post, we refer to these terminologies as TechOps. This … Read more

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. What is MLOps provides a detailed description of this concept. As ML workloads become increasingly complex and consume more energy and resources, a growing number of companies are looking for ways to manage both the costs … Read more

In June, I started a series of posts that highlight the key factors that are driving customers to choose Amazon Bedrock. The first covered building generative AI apps securely with Amazon Bedrock, while the second explored building custom generative AI applications with Amazon Bedrock. Now I’d like to take a closer look at Amazon Bedrock … Read more

This post is co-written with Francisco Azuaje from Genomics England. Genomics England analyzes sequenced genomes for The National Health Service (NHS) in the United Kingdom, and then equips researchers to use data to advance biological research. As part of its goal to help people live longer, healthier lives, Genomics England is interested in facilitating more … Read more

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 … 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 … 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 … 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 … 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 … 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 … 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 … 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, … 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 … 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 … Read more

Many ecommerce applications want to provide their users with a human-like chatbot that guides them to choose the best product as a gift for their loved ones or friends. To enhance the customer experience, the chatbot need to engage in a natural, conversational manner to understand the user’s preferences and requirements, such as the recipient’s … Read more

This post is co-written by Danilo Tommasina and Andrei Voinov from Thomson Reuters. Thomson Reuters (TR) is one of the world’s most trusted information organizations for businesses and professionals. TR provides companies with the intelligence, technology, and human expertise they need to find trusted answers, enabling them to make better decisions more quickly. TR’s customers … Read more

Generating image descriptions is a common requirement for applications across many industries. One common use case is tagging images with descriptive metadata to improve discoverability within an organization’s content repositories. Ecommerce platforms also use automatically generated image descriptions to provide customers with additional product details. Descriptive image captions also improve accessibility for users with visual … Read more

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. From deriving insights to powering generative artificial intelligence (AI)-driven applications, the ability to efficiently process and analyze large datasets is a vital capability. However, managing the complex infrastructure required for big data workloads has traditionally been a … Read more

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. This includes Meta Llama 3, Meta’s publicly available large language model (LLM). The partnership between Meta and Amazon signifies collective generative AI innovation, and Meta and Amazon are working together … Read more

Despite the ability of generative artificial intelligence (AI) to mimic human behavior, it often requires detailed instructions to generate high-quality and relevant content. Prompt engineering is the process of crafting these inputs, called prompts, that guide foundation models (FMs) and large language models (LLMs) to produce desired outputs. Prompt templates can also be used as … Read more

This post is co-written with Eliuth Triana, Abhishek Sawarkar, Jiahong Liu, Kshitiz Gupta, JR Morgan and Deepika Padmanabhan from NVIDIA.  At the 2024 NVIDIA GTC conference, we announced support for NVIDIA NIM Inference Microservices in Amazon SageMaker Inference. This integration allows you to deploy industry-leading large language models (LLMs) on SageMaker and optimize their performance and … Read more

The AWS DeepRacer League is the world’s first autonomous racing league, open to everyone and powered by machine learning (ML). AWS DeepRacer brings builders together from around the world, creating a community where you learn ML hands-on through friendly autonomous racing competitions. As we celebrate the achievements of over 560,000 participants from more than 150 … Read more