relocate MindsDB (#579)

README.md

@@ -294,7 +294,6 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [Lightwood](https://github.com/mindsdb/lightwood) ![](https://img.shields.io/github/stars/mindsdb/lightwood.svg?style=social) -  A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
 * [LIME](https://github.com/marcotcr/lime) ![](https://img.shields.io/github/stars/marcotcr/lime.svg?style=social) - Local Interpretable Model-agnostic Explanations for machine learning models.
 * [LOFO Importance](https://github.com/aerdem4/lofo-importance) ![](https://img.shields.io/github/stars/aerdem4/lofo-importance.svg?style=social) - LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.
-* [MindsDB](https://github.com/mindsdb/mindsdb) ![](https://img.shields.io/github/stars/mindsdb/mindsdb.svg?style=social) -   MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
 * [mljar-supervised](https://github.com/mljar/mljar-supervised) ![](https://img.shields.io/github/stars/mljar/mljar-supervised.svg?style=social) - A Python package for AutoML on tabular data with feature engineering, hyper-parameters tuning, explanations and automatic documentation.
 * [NETRON](https://github.com/lutzroeder/netron) ![](https://img.shields.io/github/stars/lutzroeder/netron.svg?style=social) - Viewer for neural network, deep learning and machine learning models.
 * [SHAP](https://github.com/shap/shap) ![](https://img.shields.io/github/stars/shap/shap.svg?style=social) - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
@@ -553,6 +552,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [LLMonitor](https://github.com/lunary-ai/lunary) ![](https://img.shields.io/github/stars/lunary-ai/lunary.svg?style=social) - Observability & analytics for AI apps and agents.
 * [LocalAI](https://github.com/mudler/LocalAI) ![](https://img.shields.io/github/stars/mudler/LocalAI.svg?style=social) - LocalAI is a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing.
 * [m2cgen](https://github.com/BayesWitnesses/m2cgen) ![](https://img.shields.io/github/stars/BayesWitnesses/m2cgen.svg?style=social) - A lightweight library which allows to transpile trained classic machine learning models into a native code of C, Java, Go, R, PHP, Dart, Haskell, Rust and many other programming languages.
+* [MindsDB](https://github.com/mindsdb/mindsdb) ![](https://img.shields.io/github/stars/mindsdb/mindsdb.svg?style=social) - MindsDB is the platform to create, serve, and fine-tune models in real-time from your database, vector store, and application data.
 * [MLRun](https://github.com/mlrun/mlrun)![](https://img.shields.io/github/stars/mlrun/mlrun.svg?style=social)- MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle.
 * [MLServer](https://github.com/SeldonIO/mlserver) ![](https://img.shields.io/github/stars/SeldonIO/mlserver.svg?style=social) - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more.
 * [mltrace](https://github.com/loglabs/mltrace) ![](https://img.shields.io/github/stars/loglabs/mltrace.svg?style=social) - a lightweight, open-source Python tool to get "bolt-on" observability in ML pipelines.