add AGv1.1 announcement to README (#58)

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---------

Co-authored-by: Abdul Fatir <[email protected]>

Author: canerturkmen

README.md

@@ -10,6 +10,7 @@
 
 ## 🚀 News
 
+- **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).
 - **08 Apr 2024**: 🧪 Experimental [MLX inference support](https://github.com/amazon-science/chronos-forecasting/tree/mlx) added. If you have an Apple Silicon Mac, you can now obtain significantly faster forecasts from Chronos compared to CPU inference. This provides an alternative way to exploit the GPU on your Apple Silicon Macs together with the "mps" support in PyTorch.
 - **25 Mar 2024**: [v1.1.0 released](https://github.com/amazon-science/chronos-forecasting/releases/tag/v1.1.0) with inference optimizations and `pipeline.embed` to extract encoder embeddings from Chronos.
 - **13 Mar 2024**: Chronos [paper](https://arxiv.org/abs/2403.07815) and inference code released.
@@ -63,6 +64,9 @@ To perform inference with Chronos models, install this package by running:
 ```
 pip install git+https://github.com/amazon-science/chronos-forecasting.git
 ```
+> [!TIP]  
+> The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai), which features ensembling with other statistical and machine learning models for time series forecasting 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).
+
 > [!NOTE]  
 > We have added 🧪experimental support for [MLX](https://github.com/ml-explore/mlx) inference. If you have an Apple Silicon Mac, check out the [`mlx`](https://github.com/amazon-science/chronos-forecasting/tree/mlx) branch of this repository for instructions on how to install and use the MLX version of Chronos.