AWS Machine Learning Blog

Category: Amazon SageMaker Ground Truth

How Krikey AI harnessed the power of Amazon SageMaker Ground Truth to accelerate generative AI development

This post is co-written with Jhanvi Shriram and Ketaki Shriram from Krikey. Krikey AI is revolutionizing the world of 3D animation with their innovative platform that allows anyone to generate high-quality 3D animations using just text or video inputs, without needing any prior animation experience. At the core of Krikey AI’s offering is their powerful […]

Incorporate offline and online human – machine workflows into your generative AI applications on AWS

Recent advances in artificial intelligence have led to the emergence of generative AI that can produce human-like novel content such as images, text, and audio. These models are pre-trained on massive datasets and, to sometimes fine-tuned with smaller sets of more task specific data. An important aspect of developing effective generative AI application is Reinforcement […]

Auto labeling workflow

Build an active learning pipeline for automatic annotation of images with AWS services

This blog post is co-written with Caroline Chung from Veoneer. Veoneer is a global automotive electronics company and a world leader in automotive electronic safety systems. They offer best-in-class restraint control systems and have delivered over 1 billion electronic control units and crash sensors to car manufacturers globally. The company continues to build on a […]

Skeleton-based pose annotation labeling using Amazon SageMaker Ground Truth

Pose estimation is a computer vision technique that detects a set of points on objects (such as people or vehicles) within images or videos. Pose estimation has real-world applications in sports, robotics, security, augmented reality, media and entertainment, medical applications, and more. Pose estimation models are trained on images or videos that are annotated with […]

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker

This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals and fulfills humanity’s essential needs, primarily in three markets: agriculture, food, and engineered materials. Their mining sites use industrial equipment that has to be monitored […]

Automate PDF pre-labeling for Amazon Comprehend

Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can train custom named entity recognition (NER) models to extract entities of interest, such as location, person name, and date, that are unique to their business. To train a custom model, you […]

MLOps pipeline scribble

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 1

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. Machine learning at the edge is a concept that brings the capability of running […]

Metal tag with scratches

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

In Part 1 of this series, we drafted an architecture for an end-to-end MLOps pipeline for a visual quality inspection use case at the edge. It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The focus on managed and serverless services reduces […]

Architecture diagram

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 3

This is Part 3 of our series where we design and implement an MLOps pipeline for visual quality inspection at the edge. In this post, we focus on how to automate the edge deployment part of the end-to-end MLOps pipeline. We show you how to use AWS IoT Greengrass to manage model inference at the […]

Improving your LLMs with RLHF on Amazon SageMaker

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.