awesome/machine-learning
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Free model monitoring tool added (Arize AI) for model performance management, drift detection, etc.
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@ -1747,6 +1747,7 @@ be
* [MLFlow](https://mlflow.org/) - platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
* [Weights & Biases](https://www.wandb.com/) - Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
* More tools to improve the ML lifecycle: [Catalyst](https://github.com/catalyst-team/catalyst), [PachydermIO](https://www.pachyderm.io/). The following are GitHub-alike and targeting teams [Weights & Biases](https://www.wandb.com/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/), [Valohai.ai](https://valohai.com/), [DAGsHub](https://DAGsHub.com/).
* [Arize AI](https://www.arize.com) - Model validaiton and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
* [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
* [CML](https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.