AWS Machine Learning Blog

Welcome to a New Era of Building in the Cloud with Generative AI on AWS

We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas,, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity […]

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

This is a guest post co-authored by Nafi Ahmet Turgut, Mehmet İkbal Özmen, Hasan Burak Yel, Fatma Nur Dumlupınar Keşir, Mutlu Polatcan and Emre Uzel from Getir. Getir is the pioneer of ultrafast grocery delivery. The technology company has revolutionized last-mile delivery with its grocery in-minutes delivery proposition. Getir was founded in 2015 and operates […]

Mitigate hallucinations through Retrieval Augmented Generation using Pinecone vector database & Llama-2 from Amazon SageMaker JumpStart

Despite the seemingly unstoppable adoption of LLMs across industries, they are one component of a broader technology ecosystem that is powering the new AI wave. Many conversational AI use cases require LLMs like Llama 2, Flan T5, and Bloom to respond to user queries. These models rely on parametric knowledge to answer questions. The model […]

Text Summarization Techniques

Techniques for automatic summarization of documents using language models

Summarization is the technique of condensing sizable information into a compact and meaningful form, and stands as a cornerstone of efficient communication in our information-rich age. In a world full of data, summarizing long texts into brief summaries saves time and helps make informed decisions. Summarization condenses content, saving time and improving clarity by presenting […]

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback. When prompted correctly, these models can carry coherent conversations without any task-specific training data. […]

End to end Solution Architecture

How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

This post is co-written with Stanislav Yeshchenko from Q4 Inc. Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging challenges stemming from the nature of the assortment of datasets available. These datasets are often a mix of numerical and text data, at times structured, […]

Enable faster training with Amazon SageMaker data parallel library

Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now training LLMs of unprecedented size ranging from 1 billion to over 175 billion parameters. Training these LLMs requires significant compute resources and time as hundreds […]

Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images, or social media posts, both continue to grow as they are produced and consumed by various organizations. For instance, according to International Data Corporation (IDC), […]

Foundational data protection for enterprise LLM acceleration with Protopia AI

The post describes how you can overcome the challenges of retaining data ownership and preserving data privacy while using LLMs by deploying Protopia AI’s Stained Glass Transform to protect your data. Protopia AI has partnered with AWS to deliver the critical component of data protection and ownership for secure and efficient enterprise adoption of generative AI. This post outlines the solution and demonstrates how it can be used in AWS for popular enterprise use cases like Retrieval Augmented Generation (RAG) and with state-of-the-art LLMs like Llama 2.

How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

This is a guest post co-authored by Nafi Ahmet Turgut, Hasan Burak Yel, and Damla Şentürk from Getir. Established in 2015, Getir has positioned itself as the trailblazer in the sphere of ultrafast grocery delivery. This innovative tech company has revolutionized the last-mile delivery segment with its compelling offering of “groceries in minutes.” With a […]

Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. And experienced data […]