Category: Amazon Machine Learning

Generative artificial intelligence (AI) models have opened up new possibilities for automating and enhancing software development workflows. Specifically, the emergent capability for generative models to produce code based on natural language prompts has opened many doors to how developers and DevOps professionals approach their work and improve their efficiency. In this post, we provide an […]

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. An end-to-end RAG solution involves several […]

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 […]

Information retrieval systems have powered the information age through their ability to crawl and sift through massive amounts of data and quickly return accurate and relevant results. These systems, such as search engines and databases, typically work by indexing on keywords and fields contained in data files. However, much of our data in the digital […]

This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Company earnings calls are crucial events that provide transparency into a company’s financial health and prospects. Earnings reports detail a firm’s financials over a specific period, including revenue, net income, earnings per share, balance sheet, and cash flow […]

Underwriting is a fundamental function within the insurance industry, serving as the foundation for risk assessment and management. Underwriters are responsible for evaluating insurance applications, determining the level of risk associated with each applicant, and making decisions on whether to accept or reject the application based on the insurer’s guidelines and risk appetite. In this […]

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.

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 […]

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 […]

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With […]