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Add: Truss framework for model serving

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Philip Kiely - Baseten 2024-01-24 17:31:17 -06:00 committed by GitHub
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@ -249,9 +249,10 @@ Further resources:
* [Feast](https://github.com/gojek/feast) - A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
* [Hopsworks](https://github.com/logicalclocks/hopsworks) - A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache Hive and a feature serving database, based on MySQL Cluster, for online applications.
* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform for reproducible and scalable machine learning and deep learning.
* [QuestDB](https://questdb.io/) A relational column-oriented database designed for real-time analytics on time series and event data.
* [Phoenix](https://phoenix.arize.com) Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
* [XAD](https://github.com/auto-differentiation/XAD) Comprehensive backpropagation tool for C++.
* [QuestDB](https://questdb.io/) - A relational column-oriented database designed for real-time analytics on time series and event data.
* [Phoenix](https://phoenix.arize.com) - Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
* [XAD](https://github.com/auto-differentiation/XAD) - Comprehensive backpropagation tool for C++.
* [Truss](https://truss.baseten.co) - An open source framework for packaging and serving ML models.
<a name="cpp-natural-language-processing"></a>
#### Natural Language Processing