Skater - Python Library for Model Interpretation/Explanations

•    Python

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Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive "black boxes" both for researchers and practioners. The project is still in beta phase.

ml predictive-modeling machine-learning modeling-tools model-interpretation blackbox datascience model-explanation explanation-system deep-learning deep-neural-networks attribution lstm-neural-networks cnn-classification

interpret - Fit interpretable models. Explain blackbox machine learning.

•    C++

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Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM)* which has both high accuracy and intelligibility. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. In addition to EBM, InterpretML also supports methods like LIME, SHAP, linear models, partial dependence, decision trees and rule lists. The package makes it easy to compare and contrast models to find the best one for your needs.

machine-learning interpretability gradient-boosting blackbox scikit-learn xai interpretml

cloudprober - An active monitoring software to detect failures before your customers do.

•    Go

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Cloudprober is a monitoring software that makes it super-easy to monitor availability and performance of various components of your system. Cloudprober employs the "active" monitoring model. It runs probes against (or on) your components to verify that they are working as expected. For example, it can run a probe to verify that your frontends can reach your backends. Similarly it can run a probe to verify that your in-Cloud VMs can actually reach your on-premise systems. This kind of monitoring makes it possible to monitor your systems' interfaces regardless of the implementation and helps you quickly pin down what's broken in your system.Visit Cloudprober's website at cloudprober.github.io to get started with Cloudprober.

monitoring cloud gcp ping blackbox probe prometheus grafana

madbomber - Backtrace-on-throw C++ exception logger

•    Python

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Backtrace-on-throw exception logger for debugging C++ programs. Useful in tracking down problems caused by exceptions happening in unexpected places, including those which cause threads to terminate silently.

cpp debugger debugging debugging-tool gdb stacktrace blackbox