How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Drift Detection for your PyTorch Models
The main features of torchdrift/torchdrift are: Drift Detection, Monitoring and Drift, Developer Tools.
Projects with overlapping indexed features include: seldonio/alibi-detect — Algorithms for outlier, adversarial and drift detection. online-ml/river — River is a Python framework for online machine learning, designed to train and evaluate models on streaming data. It… deepchecks/deepchecks — Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality… pycaret/pycaret — PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It… 30-seconds/30-seconds-of-code — This project is a developer knowledge base and a curated library of programming code snippets. It serves as a… 0xax/erlang-bookmarks — All about erlang programming language [powerd by community].
Algorithms for outlier, adversarial and drift detection
River is a Python framework for online machine learning, designed to train and evaluate models on streaming data. It enables incremental learning by updating model parameters one observation at a time, eliminating the need to store full training datasets in memory. The library distinguishes itself through a dedicated concept drift detection system that monitors changes in data distributions to trigger model adaptation. It also provides a progressive validation framework that simulates real-time deployment by testing models on samples before using them for training. The system covers a broad
Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality suite and performance evaluator designed to verify the integrity and performance of models and datasets from research through production. The project functions as a model monitoring tool for tracking data drift and performance degradation in production environments. It allows for the creation of custom validation suites and utilizes a pluggable check architecture to automate quality checks within continuous integration pipelines. The framework covers a broad range of capabil
PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp