Adds partial fit method to sklearn's forest estimators to allow incremental training without being limited to a linear model. Works with Dask-ml's Incremental.

Solution to kaggle competition OTTO – Multi-Objective Recommender System: https://www.kaggle.com/competitions/otto-recommender-system

Code for "Training models when data doesn't fit in memory" post

Framework for computing Machine Learning algorithms in Python using Dask and RAPIDS AI.

Dask tutorial；Dask汉化教程

Rapidsai_Machine_learnring_on_GPU

Fraud detection ML pipeline and serving POC using Dask and hopeit.engine. Project created with nbdev: https://www.fast.ai/2019/12/02/nbdev/

Scaling ML models with Taipy and Dask

A Dask native implementation of 'Term Frequency Inverse Document Frequency' for dask-ml and scikit-learn

Dask implementation of k-means|| and benchmarking

Using Dask-ML on Azure ML

Spotify-Hybrid-Recommender-System-MLOps combines collaborative and content-based filtering to deliver personalized music recommendations. Built with Streamlit and deployed using Docker, AWS EC2, Amazon ECR, CI/CD pipelines, and Blue-Green Deployment, it shows end-to-end MLOps practices, scalability, reproducibility, and production-ready ML systems.

A Parallel segmentation algorithm of a flowers dataset using Dask.

Preprocessing and predicting big data

Dask-parallelized project, contrasting GaussianNB and LightGBM models for EMNIST handwritten character classification.

pybear modules for dask

BETA: Real-time inference from scalable machine learning in Python

Saturn Cloud workshop on using LightGBM with Dask

Sentiment analysis on hotel reviews, using MongoDB, applying Dask parallel programming, comparing Recurrent and Convolutional neural networks and visualizating with Dash.