Data validation libraries added

Author: krzjoa

README.md

@@ -66,7 +66,6 @@
 * [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph structured data.
 * [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.
 * [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ### Automated Machine Learning
 * [TPOT](https://github.com/rhiever/tpot) - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -272,7 +271,7 @@
 
 ## Model Explanation
 
-* [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and eXplanation. img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
+* [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and eXplanation. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
 * [Shapley](https://github.com/benedekrozemberczki/shapley) - A data-driven framework to quantify the value of classifiers in a machine learning ensemble. 
 * [Alibi](https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining machine learning models.
 * [anchor](https://github.com/marcotcr/anchor) - Code for "High-Precision Model-Agnostic Explanations" paper.
@@ -436,11 +435,14 @@
 * [dvc](https://github.com/iterative/dvc) - Data Version Control | Git for Data & Models | ML Experiments Management.
 * [envd](https://github.com/tensorchord/envd) - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
 * [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log and reproduce experiments.
-* [Xcessiv](https://github.com/reiinakano/xcessiv) - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
-* [Persimmon](https://github.com/AlvarBer/Persimmon) - A visual dataflow programming language for sklearn.
-* [Neptune](https://neptune.ai) - A lightweight ML experiment tracking, results visualization and management tool.
 * [Ax](https://github.com/facebook/Ax) - Adaptive Experimentation Platform. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
+## Data validation
+* [great_expectations](https://github.com/great-expectations/great_expectations) - Always know what to expect from your data.
+* [pandera](https://github.com/unionai-oss/pandera) - A light-weight, flexible, and expressive statistical data testing library.
+* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [evidently](https://github.com/evidentlyai/evidently) - Evaluate and monitor ML models from validation to production.
+* [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) - Library for exploring and validating machine learning data.
 
 ## Evaluation
 * [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.