Sourcify
Effortlessly find and manage open-source dependencies for your projects.
Development
Freemium
From $99/mo
Smart Contract Verification
Dependency Management
Security Auditing
Dask-ML
4.6
Free Users praise Dask-ML for its scalability and seamless integration with existing machine learning libraries.
Scalable machine learning in Python using Dask alongside popular machine learning libraries.
0 views
0 saves
|
Refined 2/22/2026
About Dask-ML
Dask-ML provides scalable machine learning capabilities in Python by leveraging the Dask parallel computing framework. It integrates seamlessly with popular ML libraries like Scikit-Learn and XGBoost, enabling users to scale their workflows to handle larger datasets and more complex models. The tool addresses scaling challenges related to both model size and data size. For large models, Dask-ML parallelizes training, prediction, and evaluation across a Dask cluster. For large datasets, it uses Dask collections like Dask Arrays and DataFrames. Dask-ML offers estimators designed to work with these collections, including preprocessing and ensemble methods. It emphasizes a unified interface familiar to Scikit-Learn users. Dask-ML doesn't reimplement distributed solutions already available in libraries like XGBoost, instead, it facilitates their integration with Dask workflows for data preparation and deployment.
Core Capabilities
Dask-ML provides scalable machine learning capabilities in Python by leveraging the Dask parallel computing framework.
Main Tasks
Hyperparameter Optimization
Explore all tools that specialize in hyperparameter optimization. This domain focus ensures Dask-ML delivers optimized results for this specific requirement.
