IBM/sail
Library for streaming data and incremental learning algorithms.
This library helps machine learning engineers and data scientists build and manage models that learn continuously from live, incoming data streams. It provides a unified way to combine different machine learning frameworks (like Scikit-Learn or PyTorch) for online learning, taking in real-time data and outputting continuously updated model predictions. It's ideal for those needing to rapidly adapt models to changing data patterns.
Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you need to develop and deploy machine learning models that update in real-time as new data arrives, especially across different machine learning frameworks and in a distributed environment.
Not ideal if your data is static and you only need to train models once, or if you're not working with machine learning models that require continuous adaptation.
Stars
27
Forks
11
Language
Python
License
MIT
Category
Last pushed
Sep 17, 2025
Commits (30d)
0
Dependencies
13
Reverse dependents
1
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