AWS Machine Learning Blog

Category: Thought Leadership

Build generative AI applications on Amazon Bedrock — the secure, compliant, and responsible foundation

Generative AI has revolutionized industries by creating content, from text and images to audio and code. Although it can unlock numerous possibilities, integrating generative AI into applications demands meticulous planning. Amazon Bedrock is a fully managed service that provides access to large language models (LLMs) and other foundation models (FMs) from leading AI companies through a […]

Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta […]

AIML CoE Framework

Establishing an AI/ML center of excellence

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study, across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits. As maintained by Gartner, more than 80% of enterprises […]

Amazon SageMaker now integrates with Amazon DataZone to streamline machine learning governance

Unlock ML governance with SageMaker-DataZone integration: streamline infrastructure, collaborate, and govern data/ML assets.

Simple guide to training Llama 2 with AWS Trainium on Amazon SageMaker

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Their impressive generative abilities have led to widespread adoption across various sectors and use cases, including content generation, sentiment analysis, chatbot development, and virtual assistant technology. Llama2 by Meta is an example of an LLM offered by AWS. Llama […]

Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. Amazon Bedrock is a fully managed service that offers a choice […]

Fine tuning workflow

Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. In the post A generative AI-powered solution on Amazon SageMaker to help Amazon EU […]

The executive’s guide to generative AI for sustainability

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking to navigate the intersection of generative […]

Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune

In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events […]

A secure approach to generative AI with AWS

Generative artificial intelligence (AI) is transforming the customer experience in industries across the globe. Customers are building generative AI applications using large language models (LLMs) and other foundation models (FMs), which enhance customer experiences, transform operations, improve employee productivity, and create new revenue channels. The biggest concern we hear from customers as they explore the advantages of generative AI is how to protect their highly sensitive data and investments. At AWS, our top priority is safeguarding the security and confidentiality of our customers’ workloads. We think about security across the three layers of our generative AI stack …