Daekeun Kim's repositories
genai-ko-LLM
This hands-on lab walks you through a step-by-step approach to efficiently serving and fine-tuning large-scale Korean models on AWS infrastructure.
KoSimCSE-SageMaker
This is a hands-on for ML beginners to perform SimCSE step-by-step. Implemented both supervised SimCSE and unsupervisied SimCSE, and distributed training is possible with Amazon SageMaker.
sm-huggingface-kornlp
This hands-on lab guides you on how to easily train and deploy Korean NLP models in a cloud-native environment using SageMaker's Hugging Face container.
sm-kornlp-usecases
SageMaker-based fine-tuning and deployment hands-on example of a Korean NLP downstream task. Recommended for customers considering adopting NLP workloads on AWS.
sm-distributed-training-step-by-step
This repository provides hands-on labs on PyTorch-based Distributed Training and SageMaker Distributed Training. It is written to make it easy for beginners to get started, and guides you through step-by-step modifications to the code based on the most basic BERT use cases.
tfs-workshop
Deep Learning Inference hands-on labs; Learn how to host pre-trained TensorFlow/MXNet models to Amazon SageMaker Endpoint without building Docker Image
time-series-on-aws-hol
Time-series data hands-on lab on AWS for Data Scientists and Developers. Preprocessing, training and deployment using GluonTS and Amazon SageMaker.
aws-inferentia
This repository provides an easy hands-on way to get started with AWS Inferentia. A demonstration of this hands-on can be seen in the AWS Innovate 2023 - AIML Edition session.
end-to-end-pytorch-on-sagemaker
Building an end-to-end ML demo based on the PyTorch framework on SageMaker
ggv2-cv-mlops-workshop
AWS IoT Greengrass V2 Hands-on Lab for Image classification and Object Detection. It guides both how to develop artifacts from the scratch and how to to deploy your own model from public components.
sagemaker-studio-end-to-end
This hands-on lab is a Korean translated version of the official example code of Architect and build the full machine learning lifecycle with AWS. You can practice the SageMaker End-to-end pipeline in about 1 hour 30 minutes to 2 hours.
sm-distributed-train-bloom-peft-lora
This hands-on labs modifies the Hugging Face PEFT fine-tuning and model deployment example on Amazon SageMaker.
triton-multi-model-endpoint
This hands-on provides a guide to SageMaker MME(Multi-Model-Endpoint) on GPU.
aiot-e2e-sagemaker-greengrass-v2-nvidia-jetson
Hands-on lab from ML model training to model compilation to edge device model deployment on the AWS Cloud. It covers the detailed method of compiling SageMaker Neo for the target device, including cloud instance and edge device, and how to write and deploy Greengrass-v2 components from scratch.
sagemaker-distributed-training
Korean localization of the SageMaker Distributed Training notebooks added in AWS re:Invent 2020
sm-inference-new-features
SageMaker new features (multi-container endpoint, async inference, serverless inference) hands-on. It can be used more practically than the official examples, and inference examples for Korean NLP models have been added.
AWS-LLM-SageMaker
SageMaker Ployglot based RAG opensearch
amazon-sagemaker-built-in-algorithms-mlops-pipeline-using-aws-cdk
MLOps Pipeline Using SageMaker & CDK, where models are from SageMaker built-in algorithms.
autogluon-imgclass-with-sagemaker-example
This is an example of low-code AutoML using AutoGluon to perform image classification quickly and easily.
autogluon-objdetect-with-sagemaker-example
This is an example of low-code AutoML using AutoGluon to perform object detection quickly and easily.
aws-chalice-examples
Guide to basic usage of AWS Chalice
generative-ai-sagemaker-cdk-demo
Deploy Generative AI models from Amazon SageMaker JumpStart using AWS CDK
huggingface-sagemaker-workshop-series
Enterprise Scale NLP with Hugging Face & SageMaker Workshop series