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 - [Robustness Metrics](https://github.com/google-research/robustness_metrics) - Lightweight modules to evaluate the robustness of classification models.
 - [Seldon Core](https://github.com/SeldonIO/seldon-core) - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models on Kubernetes.
 - [Spark Machine Learning](https://spark.apache.org/mllib/) - Spark’s ML library consisting of common learning algorithms and utilities.
+- [Synthical](https://synthical.com) - AI-powered collaborative environment for Research. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
 - [TensorBoard](https://www.tensorflow.org/tensorboard/) - TensorFlow's Visualization Toolkit.
 - [Tensorflow Extended (TFX)](https://www.tensorflow.org/tfx/) - An end-to-end platform for deploying production ML pipelines.
 - [Tensorflow Data Validation (TFDV)](https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data. Similar to Great Expectations, but for Tensorflow data.