Add example notebook for SageMaker JumpStart

README.md

@@ -9,15 +9,19 @@
 [![preprint](https://img.shields.io/static/v1?label=arXiv&message=2403.07815&color=B31B1B&logo=arXiv)](https://arxiv.org/abs/2403.07815)
 [![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Datasets-FFD21E)](https://huggingface.co/datasets/autogluon/chronos_datasets)
 [![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444)
+[![fev](https://img.shields.io/static/v1?label=fev&message=Benchmark&color=B31B1B&logo=github)](https://github.com/autogluon/fev)
+[![aws](https://img.shields.io/static/v1?label=SageMaker&message=Deploy&color=FF9900&logo=amazon-web-services)](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb)
 [![faq](https://img.shields.io/badge/FAQ-Questions%3F-blue)](https://github.com/amazon-science/chronos-forecasting/issues?q=is%3Aissue+label%3AFAQ)
 [![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
 
 </div>
 
-## 🚀 News
 
-- **26 Nov 2024**: ⚡️ Chronos-Bolt models released [on HuggingFace](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444). Chronos-Bolt models are more accurate (5% lower error), up to 250x faster and 20x more memory efficient than the original Chronos models of the same size! 
-- **27 Jun 2024**: 🚀 [Released datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) used in the paper and an [evaluation script](./scripts/README.md#evaluating-chronos-models) to compute the WQL and MASE scores reported in the paper. 
+## 🚀 News
+- **14 Feb 2024**: 🚀 Chronos-Bolt is now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos endpoints for production use in 3 lines of code.
+- **12 Dec 2024**: 📊 We released [`fev`](https://github.com/autogluon/fev), a lightweight package for benchmarking time series forecasting models based on the [Hugging Face `datasets`](https://huggingface.co/docs/datasets/en/index) library.
+- **26 Nov 2024**: ⚡️ Chronos-Bolt models released [on HuggingFace](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444). Chronos-Bolt models are more accurate (5% lower error), up to 250x faster and 20x more memory efficient than the original Chronos models of the same size!
+- **27 Jun 2024**: 🚀 [Released datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) used in the paper and an [evaluation script](./scripts/README.md#evaluating-chronos-models) to compute the WQL and MASE scores reported in the paper.
 - **17 May 2024**: 🐛 Fixed an off-by-one error in bin indices in the `output_transform`. This simple fix significantly improves the overall performance of Chronos. We will update the results in the next revision on ArXiv.
 - **10 May 2024**: 🚀 We added the code for pretraining and fine-tuning Chronos models. You can find it in [this folder](./scripts/training). We also added [a script](./scripts/kernel-synth.py) for generating synthetic time series data from Gaussian processes (KernelSynth; see Section 4.2 in the paper for details). Check out the [usage examples](./scripts/).
 - **19 Apr 2024**: 🚀 Chronos is now supported on [AutoGluon-TimeSeries](https://auto.gluon.ai/stable/tutorials/timeseries/index.html), the powerful AutoML package for time series forecasting which enables model ensembles, cloud deployments, and much more. Get started with the [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
@@ -61,7 +65,7 @@ The models in this repository are based on the [T5 architecture](https://arxiv.o
 
 ### Zero-Shot Results
 
-The following figure showcases the remarkable **zero-shot** performance of Chronos and Chronos-Bolt models on 27 datasets against local models, task-specific models and other pretrained models. For details on the evaluation setup and other results, please refer to [the paper](https://arxiv.org/abs/2403.07815). 
+The following figure showcases the remarkable **zero-shot** performance of Chronos and Chronos-Bolt models on 27 datasets against local models, task-specific models and other pretrained models. For details on the evaluation setup and other results, please refer to [the paper](https://arxiv.org/abs/2403.07815).
 
 <p align="center">
   <img src="https://raw.githubusercontent.com/amazon-science/chronos-forecasting/main/figures/zero_shot-agg_scaled_score.svg" width="100%">
@@ -89,8 +93,10 @@ git clone https://github.com/amazon-science/chronos-forecasting.git
 cd chronos-forecasting && pip install --editable ".[training]"
 ```
 
-> [!TIP]  
-> This repository is intended for research purposes and provides a minimal interface to Chronos models. The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai), which features effortless fine-tuning, augmenting Chronos models with exogenous information through covariate regressors, ensembling with other statistical and machine learning models, as well as seamless deployments on AWS with SageMaker 🧠. Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
+> [!TIP]
+> This repository is intended for research purposes and provides a minimal interface to Chronos models. For reliable production use, we recommend the following options:
+> - [AutoGluon](https://auto.gluon.ai) provides effortless fine-tuning, augmenting Chronos models with exogenous information through covariate regressors, ensembling with other statistical and machine learning models. Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
+> - SageMaker JumpStart makes it easy to deploy Chronos inference endpoints to AWS with just a few lines of code. Check out [this tutorial](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb) for more details.
 
 ### Forecasting