AWS Machine Learning Blog

Category: Artificial Intelligence

Recommend and dynamically filter items based on user context in Amazon Personalize

Organizations are continuously investing time and effort in developing intelligent recommendation solutions to serve customized and relevant content to their users. The goals can be many: transform the user experience, generate meaningful interaction, and drive content consumption. Some of these solutions use common machine learning (ML) models built on historical interaction patterns, user demographic attributes, […]

Interactively fine-tune Falcon-40B and other LLMs on Amazon SageMaker Studio notebooks using QLoRA

Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models. We utilize Hugging Face’s parameter-efficient fine-tuning (PEFT) library and quantization techniques through bitsandbytes to support interactive fine-tuning of […]

Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas

Public health organizations have a wealth of data about different types of diseases, health trends, and risk factors. Their staff has long used statistical models and regression analyses to make important decisions such as targeting populations with the highest risk factors for a disease with therapeutics, or forecasting the progression of concerning outbreaks. When public […]

Safe image generation and diffusion models with Amazon AI content moderation services

Generative AI technology is improving rapidly, and it’s now possible to generate text and images based on text input. Stable Diffusion is a text-to-image model that empowers you to create photorealistic applications. You can easily generate images from text using Stable Diffusion models through Amazon SageMaker JumpStart. The following are examples of input texts and […]

Use proprietary foundation models from Amazon SageMaker JumpStart in Amazon SageMaker Studio

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications. SageMaker JumpStart allows you to deploy foundation models from a network isolated environment, and […]

How and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Machine Learning Operations (MLOps) provides the technical solution to this issue, assisting organizations in managing, […]

Define customized permissions in minutes with Amazon SageMaker Role Manager via the AWS CDK

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. Their primary focus is to ensure that users operate with the utmost security, adhering to the principle of least privilege. However, accommodating the diverse needs of different user personas and creating appropriate permission policies can sometimes impede agility. […]

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code. SageMaker Data Wrangler supports Snowflake, a popular […]

Deploy a serverless ML inference endpoint of large language models using FastAPI, AWS Lambda, and AWS CDK

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in other applications. It can be cumbersome to manage the process, but with the right tool, […]

customized neural network model architecture

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc. is the leading cross-platform global game company that provides gambling products and services. Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to […]