Parallel computing library that scales Python analytics and scientific computing workloads from laptops to clusters.

Dask

Parallel DataFrames: Scale pandas workflows
Distributed Arrays: NumPy at scale
Task Scheduling: Intelligent work distribution
ML Integration: Works with scikit-learn and XGBoost
Dashboard: Real-time monitoring

Flexible library for parallel computing in Python. Scales pandas, NumPy, and scikit-learn workflows to multi-core machines and distributed clusters.

Key Features

Used Dask for processing large-scale datasets, parallel ML training, and distributed data transformations in production pipelines.