Category: Artificial Intelligence

June 2023: This post was updated to cover the Amazon Kendra Retrieve API optimized for RAG use cases, and Amazon Kendra retriever now being part of the LangChain GitHub repo. This revision also updates the instructions to use new version samples from the AWS Samples GitHub repo. Generative AI (GenAI) and large language models (LLMs), […]

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. Combined with their flexibility, high speed of development, and low operating cost, these general-purpose processors offer an alternative to other existing hardware solutions. AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 inference […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. It provides a single, web-based visual interface where you can perform all machine learning (ML) development steps required to build, train, tune, debug, deploy, and monitor models. It gives data scientists all the tools you need to take ML models from experimentation […]

This post is co-written by Jyoti Sharma and Sharmo Sarkar from Vericast. For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. […]

One of the most popular models available today is XGBoost. With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. In this post, we dive deep to see how Amazon SageMaker can serve these models using NVIDIA Triton […]

Machine learning (ML) helps organizations generate revenue, reduce costs, mitigate risk, drive efficiencies, and improve quality by optimizing core business functions across multiple business units such as marketing, manufacturing, operations, sales, finance, and customer service. With AWS ML, organizations can accelerate the value creation from months to days. Amazon SageMaker Canvas is a visual, point-and-click […]

Today, we announce the availability of sample notebooks that demonstrate question answering tasks using a Retrieval Augmented Generation (RAG)-based approach with large language models (LLMs) in Amazon SageMaker JumpStart. Text generation using RAG with LLMs enables you to generate domain-specific text outputs by supplying specific external data as part of the context fed to LLMs. […]

The rapid adoption of smart phones and other mobile platforms has generated an enormous amount of image data. According to Gartner, unstructured data now represents 80–90% of all new enterprise data, but just 18% of organizations are taking advantage of this data. This is mainly due to a lack of expertise and the large amount […]

Recent large language models (LLMs) have enabled tremendous progress in natural language understanding. However, they are prone to generating confident but nonsensical explanations, which poses a significant obstacle to establishing trust with users. In this post, we show how to incorporate human feedback on the incorrect reasoning chains for multi-hop reasoning to improve performance on […]

Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by extending the functionality of existing machine learning (ML) frameworks such as PyTorch. In general, an operator describes […]