adaptive-machine-learning / CapyMOA
CapyMOA does efficient machine learning for data streams in Python. CapyMOA is a toolbox of methods and evaluators for: classification, regression, clustering, anomaly detection, semi-supervised learning, online continual learning, and drift detection for data streams.
Why this job: matched by what the repo talks about: learn, data, machine, anomaly
What it does
The deep-learning parts of CapyMOA (capymoa.ocl, capymoa.core.torch.ann, the Batch* learners) need PyTorch, which is an optional extra:
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 4 days ago
- Stars
- 153
- +1 this week
- Licence
- BSD-3-Clause
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.15.1
- Released 48 days ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 95% closed
- 2 still open
- Pull requests
- 352 merged
- 13 waiting
- Releases
- 23
- 5 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 100%
- Jinja <1%
- Dockerfile <1%
- Java <1%
Topics
- anomaly-detection
- concept-drift
- continual-learning
- data-science
- data-shift
- deep-learning
- drift-detection
- incremental-learning
- machine-learning
- online-classification
- online-clustering
- online-continual-learning
Project record
- JobEvals and observability
- StatusListed
- Commits681
- Forks51
- Watching9
- Stars in 28 days+1
- CreatedSep 1, 2023
- First seen by usNov 7, 2026
- CheckedNov 9, 2026