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‎README.md‎

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@@ -26,6 +26,7 @@ End-to-end example use-cases built using modules in this repository.
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| Type | Description |
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| [MLOps with Amazon SageMaker](manifests/mlops-sagemaker/) | Set up environment for MLOps with Amazon SageMaker. Deploy secure Amazon SageMaker Studio Domain, and provisions SageMaker Project Templates using Service Catalog, including model training and deployment. |
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| [Amazon SageMaker HyperPod on Amazon EKS](manifests/hyperpod-eks/) | Deploy Amazon SageMaker HyperPod cluster orchestrated by Amazon EKS with FSx Lustre for high-performance distributed training workloads. |
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| [Ray on Amazon Elastic Kubernetes Service (EKS)](manifests/ray-on-eks/) | Run Ray on AWS EKS. Deploys an AWS EKS cluster, KubeRay Ray Operator, and a Ray Cluster with autoscaling enabled. |
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| [Fine-tune 6B LLM (GPT-J) using Ray on Amazon EKS](manifests/fine-tuning-6b/) | Run fine-tuning of 6B GPT-J LLM. Deploys an AWS EKS cluster, KubeRay Ray Operator, and a Ray Cluster with autoscaling enabled, and runs a fine-tuning job. [How to fine tune a 6B LLM simply and cost-effective using Ray on Amazon EKS?](https://medium.com/@junjie-tang/how-to-fine-tune-a-6b-llm-simply-and-cost-effective-using-ray-ai-runtime-on-amazon-eks-a4ae0b3c6464) |
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| [DeepSeek R1 on Amazon SageMaker](manifests/deepseek-sagemaker/) | An example using [DeepSeek R1 Distill Llama 8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) on Amazon SageMaker. Deploys a VPC, and Amazon SageMaker endpoint and Amazon SageMaker Studio IDE. |
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| Type | Description |
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| [SageMaker Studio Module](modules/sagemaker/sagemaker-studio/README.md) | Provisions secure SageMaker Studio Domain environment, creates example User Profiles for Data Scientist and Lead Data Scientist linked to IAM Roles, and adds lifecycle config |
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| [SageMaker Endpoint Module](modules/sagemaker/sagemaker-endpoint/README.md) | Creates SageMaker real-time inference endpoint for the specified model package or latest approved model from the model package group |
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| [SageMaker HyperPod EKS Module](modules/sagemaker/hyperpod-eks-tf/README.md) | Creates a complete Amazon SageMaker HyperPod cluster infrastructure orchestrated by Amazon EKS. Deploys networking components, EKS cluster, HyperPod cluster, and supporting resources including FSx Lustre for high-performance storage. |
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| [SageMaker Project Templates Factory Module](modules/sagemaker/sagemaker-templates/README.md) | Provisions SageMaker Project Templates for an organization based on the specified template type. Available templates:<br/> - [Train a model on Abalone dataset using XGBoost](modules/sagemaker/sagemaker-templates/README.md#train-a-model-on-abalone-dataset-with-xgboost-template)<br/>- [Perform batch inference](modules/sagemaker/sagemaker-templates/README.md#batch-inference-template)<br/>- [Multi-account model deployment](modules/sagemaker/sagemaker-templates/README.md#multi-account-model-deployment-template) <br/>- [HuggingFace model import template](modules/sagemaker/sagemaker-templates/README.md#huggingface-model-import-template) <br/>- [LLM fine-tuning and evaluation](modules/sagemaker/sagemaker-templates/README.md#llm-evaluate-template) |
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| [SageMaker Notebook Instance Module](modules/sagemaker/sagemaker-notebook/README.md) | Creates secure SageMaker Notebook Instance for the Data Scientist, clones the source code to the workspace |
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| [SageMaker Ground Truth Labeling Module](modules/sagemaker/sagemaker-ground-truth-labeling/README.md) | Creates a state machine to allow labeling of images and text file, uploaded to the upload bucket, using various built-in task types in SageMaker Ground Truth. |
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| [SageMaker Model Monitoring Module](modules/sagemaker/sagemaker-model-monitoring/README.md) | Deploy data quality, model quality, model bias, and model explainability monitoring jobs which run against a SageMaker Endpoint. |
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| [SageMaker Endpoint Module](modules/sagemaker/sagemaker-endpoint/README.md) | Creates SageMaker real-time inference endpoint for the specified model package or latest approved model from the model package group |
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| [SageMaker Custom Kernel Module](modules/sagemaker/sagemaker-custom-kernel/README.md) | Builds custom kernel for SageMaker Studio from a Dockerfile |
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| [SageMaker Model Package Group Module](modules/sagemaker/sagemaker-model-package-group/README.md) | Creates a SageMaker Model Package Group to register and version SageMaker Machine Learning (ML) models and setups an Amazon EventBridge Rule to send model package group state change events to an Amazon EventBridge Bus |
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| [SageMaker Model Package Promote Pipeline Module](modules/sagemaker/sagemaker-model-package-promote-pipeline/README.md) | Deploy a Pipeline to promote SageMaker Model Packages in a multi-account setup. The pipeline can be triggered through an EventBridge rule in reaction of a SageMaker Model Package Group state event change (Approved/Rejected). Once the pipeline is triggered, it will promote the latest approved model package, if one is found. |
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| [SageMaker Model Monitoring Module](modules/sagemaker/sagemaker-model-monitoring/README.md) | Deploy data quality, model quality, model bias, and model explainability monitoring jobs which run against a SageMaker Endpoint. |
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| [SageMaker Model CICD Module](modules/sagemaker/sagemaker-model-cicd/README.md) | Creates a comprehensive CICD pipeline using AWS CodePipelines to build and deploy a ML model on SageMaker. |
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| [SageMaker Ground Truth Labeling Module](modules/sagemaker/sagemaker-ground-truth-labeling/README.md) | Creates a state machine to allow labeling of images and text file, uploaded to the upload bucket, using various built-in task types in SageMaker Ground Truth. |
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### Mlflow Modules
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