AWS Machine Learning Blog
Category: Learning Levels
Improve your Stable Diffusion prompts with Retrieval Augmented Generation
Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. In November […]
Create summaries of recordings using generative AI with Amazon Bedrock and Amazon Transcribe
October 2024: The contents of this post are outdated. Please refer to Summarize call transcriptions securely with Amazon Transcribe and Amazon Bedrock Guardrails for latest solution and code artifacts. Meeting notes are a crucial part of collaboration, yet they often fall through the cracks. Between leading discussions, listening closely, and typing notes, it’s easy for […]
Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions
Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building a robust MLOps pipeline demands cross-functional […]
Create a web UI to interact with LLMs using Amazon SageMaker JumpStart
The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can use this technology to create new products and services on AWS, such as enterprise chatbots, which are more conversational. This post shows you how you can create a web UI, which […]
Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium
Large language models (or LLMs) have become a topic of daily conversations. Their quick adoption is evident by the amount of time required to reach a 100 million users, which has gone from “4.5yrs by facebook” to an all-time low of mere “2 months by ChatGPT.” A generative pre-trained transformer (GPT) uses causal autoregressive updates […]
Vodafone advances its machine learning skills with AWS DeepRacer and Accenture
Vodafone is transitioning from a telecommunications company (telco) to a technology company (TechCo) by 2025, with objectives of innovating faster, reducing costs, improving security, and simplifying operations. Thousands of engineers are being onboarded to contribute to this transition. By 2025, Vodafone plans to have 50% of its global workforce actively involved in software development, with […]
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 […]
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), […]