Pinned Repositories
amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
amazon-sagemaker-immersion-day
amazon-sagemaker-jumpstart-genai
amazon-sagemaker-secure-mlops
amazon-sagemaker-studio-vpc-blog
amazon-sagemaker-studio-vpc-networkfirewall
This solution demonstrates the setup and deployment of Amazon SageMaker Studio into a private VPC and implementation of multi-layer security controls, such as data encryption, network traffic monitoring and restriction, usage of VPC endpoints, subnets and security groups, IAM resource policies.
anycommerce
E-Commerce demo application.
AOAI-workshop-kr
sagemaker-basic
sm-model-serving-patterns
SageMaker Model Serving Patterns
hyogrin's Repositories
hyogrin/amazon-sagemaker-jumpstart-genai
hyogrin/sagemaker-basic
hyogrin/sm-model-serving-patterns
SageMaker Model Serving Patterns
hyogrin/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
hyogrin/amazon-sagemaker-immersion-day
hyogrin/amazon-sagemaker-secure-mlops
hyogrin/amazon-sagemaker-studio-vpc-blog
hyogrin/amazon-sagemaker-studio-vpc-networkfirewall
This solution demonstrates the setup and deployment of Amazon SageMaker Studio into a private VPC and implementation of multi-layer security controls, such as data encryption, network traffic monitoring and restriction, usage of VPC endpoints, subnets and security groups, IAM resource policies.
hyogrin/anycommerce
E-Commerce demo application.
hyogrin/AOAI-workshop-kr
hyogrin/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
hyogrin/aws-ai-ml-workshop-kr
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
hyogrin/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.
hyogrin/aws-neuron-samples
Example code for AWS Neuron SDK developers building inference and training applications
hyogrin/azureai-samples
Official community-driven Azure AI Examples
hyogrin/creative-assistant-with-genai-on-sagemaker
hyogrin/eks-ha-dr_aws-terraform
cx eks DR test
hyogrin/fraud-detector-workshop
hyogrin/generate-rotten-vegetable-pjt
hyogrin/keras-mmoe
A TensorFlow Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)
hyogrin/llmops-promptflow-template
LLMOps with Prompt Flow is a "LLMOps template and guidance" to help you build LLM-infused apps using Prompt Flow. It offers a range of features including Centralized Code Hosting, Lifecycle Management, Variant and Hyperparameter Experimentation, A/B Deployment, reporting for all runs and experiments and so on.
hyogrin/optimum-benchmark
A unified multi-backend utility for benchmarking Transformers, Timm, Diffusers and Sentence-Transformers with full support of Optimum's hardware optimizations & quantization schemes.
hyogrin/optimum-neuron
Easy, fast and very cheap training and inference on AWS Trainium and Inferentia chips.
hyogrin/sagemaker-recommend
hyogrin/Self-Study-On-SageMaker
hyogrin/sm-pipeline
SageMaker pipeline with abalone dataset
hyogrin/tensorflow
An Open Source Machine Learning Framework for Everyone
hyogrin/terraform-aws-sagemaker
Terraform Module: Amazon SageMaker
hyogrin/triton_python_backend
Triton backend that enables pre-process, post-processing and other logic to be implemented in Python.
hyogrin/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs