Avalanche vs CML
A side-by-side comparison of two Model Training and Orchestration AI agents — to help you pick the right one.
Avalanche
Avalanche is an open-source library designed for continual learning in AI, enabling researchers and developers to prototype, train, and evaluate algorithms that adapt to new data over time without forgetting previous knowledge. It provides a modular framework for benchmarking and deploying continual learning models across various scenarios.
CML
CML (Continuous Machine Learning) is an open-source library that integrates continuous integration and delivery (CI/CD) workflows into machine learning projects, enabling automated testing, training, and deployment of models. It provides tools for versioning datasets, tracking experiments, and generating reports within CI pipelines.
| Avalanche | CML | |
|---|---|---|
| Category | Model Training and Orchestration | Model Training and Orchestration |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
Avalanche: what it solves
It tackles catastrophic forgetting in machine learning models, allowing them to learn incrementally from new data while retaining performance on previously learned tasks.
CML: what it solves
It automates and streamlines the MLOps workflow by bridging the gap between traditional CI/CD practices and machine learning development, reducing manual overhead in model iteration and deployment.