Projects sharing features with Towhee

Serverless proxy for Spark cluster

A general purpose recommender metrics library for fair evaluation.

Featuretools is an automated feature engineering library and data transformation framework written in Python. It automatically generates machine learning feature vectors from multi-table datasets by applying synthesis patterns to relational and timestamped data. The system functions as a distributed feature synthesis engine, allowing the process of creating feature vectors to scale across multiple cores or clusters to handle large-scale datasets. The library supports the synthesis of multi-table datasets, time series feature generation, and the creation of custom machine learning primitives

Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin

Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.

splearn: package for signal processing and machine learning with Python. Contains tutorials on understanding and applying signal processing.

This project is a curated collection of technical reference materials and study guides designed for machine learning interview preparation. It provides comprehensive resources for candidates pursuing engineering roles, focusing on deep learning, production infrastructure, and large-scale system design. The repository distinguishes itself through an architecture that combines theoretical research with industrial case studies. It utilizes a pattern-based approach to system design, breaking down complex deployments—such as recommendation engines, search ranking, and ad click prediction—into reus

🐍 Quick reference guide to common patterns & functions in PySpark.

DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi

source code from the book Genetic Algorithms with Python by Clinton Sheppard

🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Template repository for data science lifecycle project

Examples of Machine Learning code using Comet.ml

Comet LLM is an observability platform and evaluation framework designed for large language model applications and agentic workflows. It functions as a system for tracing, monitoring, and debugging execution flows while providing tools for prompt optimization and the enforcement of AI safety guardrails. The platform distinguishes itself through a combination of model-based scoring and heuristic metrics to quantify output quality and detect hallucinations. It includes a dedicated prompt and agent optimizer with an interactive playground for refining templates and tool configurations. For retri

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

Feature engineering and machine learning: together at last!

Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users

CML is a pipeline automation tool for training and evaluating machine learning models, functioning as a CI/CD system for machine learning. It serves as a cloud compute orchestrator and Git-based workflow manager that automates model training cycles through branch management, automated commits, and integrated reporting. The project distinguishes itself by provisioning ephemeral cloud instances or Kubernetes nodes to provide specialized hardware for compute-heavy tasks. It also manages remote compute runners, allowing the connection of self-hosted GPU clusters or on-premise machines to execute

Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022

Ways of doing Data Science Engineering and Machine Learning in R and Python

Julia kernel for Jupyter

MindsDB is an AI-native database engine that treats machine learning models and autonomous agents as virtual tables. By mapping external data sources, predictive models, and third-party services directly into the database schema, it enables users to perform inference, data retrieval, and complex orchestration using standard SQL syntax. The platform distinguishes itself through an autonomous agent orchestrator that executes iterative reasoning loops, allowing agents to plan data access and synthesize natural language responses from connected knowledge bases. It functions as a federated data ga