AWS Machine Learning Blog With the advent of generative artificial intelligence (AI), foundation models (FMs) can generate content such as answering questions, summarizing text, and providing highlights from the sourced document. However, for model selection, there is a wide choice from model providers, like Amazon, Anthropic, AI21 Labs, Cohere, and Meta, coupled with discrete real-world […]Continue reading
AWS Machine Learning Blog Capital markets operation teams face numerous challenges throughout the post-trade lifecycle, including delays in trade settlements, booking errors, and inaccurate regulatory reporting. For derivative trades, it’s even more challenging. The timely settlement of derivative trades is an onerous task. This is because trades involve different counterparties and there is a high […]Continue reading
AWS Machine Learning Blog This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Financial analysts and research analysts in capital markets distill business insights from […]Continue reading
AWS Machine Learning Blog We are excited to announce the availability of the Jamba-Instruct large language model (LLM) in Amazon Bedrock. Jamba-Instruct is built by AI21 Labs, and most notably supports a 256,000-token context window, making it especially useful for processing large documents and complex Retrieval Augmented Generation (RAG) applications. What is Jamba-Instruct Jamba-Instruct is […]Continue reading
AWS Machine Learning Blog Amazon Web Services is excited to announce the launch of the AWS Neuron Monitor container, an innovative tool designed to enhance the monitoring capabilities of AWS Inferentia and AWS Trainium chips on Amazon Elastic Kubernetes Service (Amazon EKS). This solution simplifies the integration of advanced monitoring tools such as Prometheus and […]Continue reading
AWS Machine Learning Blog Extracting valuable insights from customer feedback presents several significant challenges. Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Scalability becomes an issue as the amount of feedback grows, hindering the ability to respond promptly and […]Continue reading
AWS Machine Learning Blog Large language models (LLMs) enable remarkably human-like conversations, allowing builders to create novel applications. LLMs find use in chatbots for customer service, virtual assistants, content generation, and much more. However, the implementation of LLMs without proper caution can lead to the dissemination of misinformation, manipulation of individuals, and the generation of […]Continue reading
AWS Machine Learning Blog Amazon Bedrock has enabled customers to build new delightful experiences for their customers using generative artificial intelligence (AI). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a […]Continue reading
AWS Machine Learning Blog This post is a continuation of Creating Natural Conversations with Amazon Lex QnAIntent and Amazon Bedrock Knowledge Base. In summary, we explored new capabilities available through Amazon Lex QnAIntent, powered by Amazon Bedrock, that enable you to harness natural language understanding and your own knowledge repositories to provide real-time, conversational experiences. […]Continue reading
AWS Machine Learning Blog This post is co-written with Jhanvi Shriram and Ketaki Shriram from Krikey. Krikey AI is revolutionizing the world of 3D animation with their innovative platform that allows anyone to generate high-quality 3D animations using just text or video inputs, without needing any prior animation experience. At the core of Krikey AI’s […]Continue reading
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