AWS Machine Learning Blog
Category: Advanced (300)
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 […]
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, […]
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), […]
Amazon SageMaker simplifies setting up SageMaker domain for enterprises to onboard their users to SageMaker
As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities, […]
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. SageMaker provides […]
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. Although […]
Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1
As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that support multiple tenants—for data scientists internal to their organization and external customers. More and more companies are realizing the value of using FMs to generate […]
Accelerate deep learning model training up to 35% with Amazon SageMaker smart sifting
In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. However, the increasing cost associated with training and fine-tuning these models poses a challenge for enterprises. This cost is primarily driven by the […]
Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically run notebooks as jobs […]