Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio

Favorite Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and issues. These iterative challenges can

Read More
Shared by AWS Machine Learning September 24, 2024

Making traffic lights more efficient with Amazon Rekognition

Favorite State and local agencies spend approximately $1.23 billion annually to operate and maintain signalized traffic intersections. On the other end, traffic congestion at intersections costs drivers about $22 billion annually. Implementing an artificial intelligence (AI)-powered detection-based solution can significantly mitigate congestion at intersections and reduce operation and maintenance costs. In

Read More
Shared by AWS Machine Learning September 24, 2024

Enhancing Just Walk Out technology with multi-modal AI

Favorite Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. You can find this checkout-free technology in over 180 third-party locations worldwide, including travel retailers,

Read More
Shared by AWS Machine Learning September 24, 2024

Build a generative AI assistant to enhance employee experience using Amazon Q Business

Favorite In today’s fast-paced business environment, organizations are constantly seeking innovative ways to enhance employee experience and productivity. There are many challenges that can impact employee productivity, such as cumbersome search experiences or finding specific information across an organization’s vast knowledge bases. Additionally, with the rise of remote and hybrid

Read More
Shared by AWS Machine Learning September 21, 2024

Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

Favorite Amazon Bedrock Agents offers developers the ability to build and configure autonomous agents in their applications. These agents help users complete actions based on organizational data and user input, orchestrating interactions between foundation models (FMs), data sources, software applications, and user conversations. Amazon Bedrock agents use the power of

Read More
Shared by AWS Machine Learning September 21, 2024

Integrate Amazon Bedrock Knowledge Bases with Microsoft SharePoint as a data source

Favorite Amazon Bedrock Knowledge Bases provides foundation models (FMs) and agents in Amazon Bedrock contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. Amazon Bedrock Knowledge Bases offers a fully managed RAG experience. The data sources that can

Read More
Shared by AWS Machine Learning September 20, 2024

Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker

Favorite This post is co-written with Meta’s PyTorch team. In today’s rapidly evolving AI landscape, businesses are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. Although foundation models (FMs) offer impressive out-of-the-box capabilities, true competitive advantage often lies in deep model customization through fine-tuning.

Read More
Shared by AWS Machine Learning September 20, 2024