Managing cloud costs and understanding resource usage can be a daunting task, especially for organizations with complex AWS deployments. AWS Cost and Usage Reports (AWS CUR) provides valuable data insights, but interpreting and querying the raw data can be challenging. In this post, we explore a solution that uses generativeContinue Reading
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 trainedContinue Reading
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 betterContinue Reading
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 improveContinue Reading
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 560,000Continue Reading
Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and generative language models. RAG models retrieve relevant information from a large corpus of text and then use a generative language model to synthesize an answer based on the retrieved information.Continue Reading
Several factors can make remediating security findings challenging. First, the sheer volume and complexity of findings can overwhelm security teams, leading to delays in addressing critical issues. Findings often require a deep understanding of AWS services and configurations and require many cycles for validation, making it more difficult for lessContinue Reading
At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. We envision a future where AI seamlessly integrates into our teams’ workflows, automating repetitive tasks, providing intelligent recommendations, and freeing up time for more strategic, high-value interactions. Our field organizationContinue Reading
Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and foundation models (FMs). RAG models first retrieve relevant information from a large corpus of text and then use a FM to synthesize an answer based on the retrieved information. AnContinue Reading
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 ofContinue Reading
