Search Results for "python data analysis" - Page 8
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Showing 1057 open source projects for "python data analysis"
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Cloud-Based Software Licensing - Zentitle by Nalpeiron1000’s software companies have used Zentitle to launch new software products fast and control their entitlements easily - many going from startup to IPO on our platform. Our software monetization infrastructure allows you to easily build or
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
Pixeltable is an open-source Python data infrastructure framework designed to support the development of multimodal AI applications. The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. ... -
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MiroFlow
Agent framework that enables tool-use agent tasks
MiroFlow is a high-performance open-source framework designed for building intelligent AI agents capable of solving complex reasoning and research tasks. The system introduces a hierarchical architecture that organizes components into control, agent, and foundation layers, allowing developers to manage agent orchestration and tool interactions in a structured manner. One of the core innovations of MiroFlow is its use of agent graphs, which enable flexible orchestration of multiple sub-agents... -
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Claude Scientific Skills
A set of ready to use Agent Skills for research, science, engineering
Claude Scientific Skills is a large open source collection of ready-to-use scientific capabilities that extend AI coding agents into full research assistants. The project provides more than 170 curated skills covering domains such as genomics, drug discovery, medical imaging, physics, and advanced data analysis. Each skill bundles documentation, examples, and tool integrations so agents can reliably execute complex multi-step scientific workflows. The framework follows the open Agent Skills standard and works with multiple AI development environments including Claude Code, Cursor, and Codex. Its primary goal is to reduce the friction of scientific computing by giving AI agents structured access to specialized libraries, databases, and research pipelines. ... -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
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DeepAudit
AI multi-agent platform for automated code security auditing system
DeepAudit is an open source code security auditing platform that uses a multi-agent architecture to analyze and identify vulnerabilities in software projects. Instead of relying solely on traditional static analysis, it simulates the reasoning process of security experts through coordinated agents responsible for orchestration, reconnaissance, analysis, and verification. DeepAudit performs deep semantic understanding of code, enabling it to detect complex vulnerabilities that span multiple... -
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
Instill Core is an open-source, full-stack AI infrastructure platform designed to orchestrate data pipelines, machine learning models, and unstructured data processing into a unified, production-ready system. It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. The platform focuses heavily on handling unstructured data such as documents,... -
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Superlinked
Superlinked is a Python framework for AI Engineers
Superlinked is a Python framework designed for AI engineers to build high-performance search and recommendation applications that combine structured and unstructured data. -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). -
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supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
Zendesk: The Complete Customer Service SolutionEquip your agents with powerful AI tools and workflows that boost efficiency and elevate customer experiences across every channel.
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X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
X-AnyLabeling is an open-source data annotation platform designed to streamline the process of labeling datasets for computer vision and multimodal AI applications. The software integrates an AI-powered labeling engine that allows users to generate annotations automatically with the assistance of modern vision models such as Segment Anything and various object detection frameworks. It supports labeling tasks across images and videos and enables developers to prepare training datasets for... -
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LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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RAGFlow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data. -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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Chroma MCP
A Model Context Protocol (MCP) server implementation
Chroma MCP Server is an implementation of the Model Context Protocol (MCP) designed to integrate large language model (LLM) applications with external data sources or tools. It offers a standardized framework to seamlessly provide LLMs with the context they require for effective operation. -
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GPTme
Your agent in your terminal, equipped with local tools
GPTMe is a personal AI chatbot designed for self-reflection, journaling, and productivity, using GPT models to generate personalized insights and responses. -
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YData Synthetic
Synthetic data generators for tabular and time-series data
...It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for... -
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Dendrite
Tools to build web AI agents that can authenticate
Dendrite Python SDK is a toolkit for building web AI agents that can authenticate, interact with, and extract data from any website, facilitating web automation tasks. -
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Meshwork Analysis terminal
Quantitative analytical Finance Terminal
MWA Finance Terminal allows users to research and obtain data about various financial instruments and allows them to perform Statistical analysis with Various ML Technologies. It's divided into Modules and Options within them. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,... -
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PySINDy
A package for the sparse identification of nonlinear dynamical systems
PySINDy is a Python library that implements the Sparse Identification of Nonlinear Dynamics (SINDy) method for discovering mathematical models of dynamical systems from data. The framework focuses on identifying governing equations that describe the behavior of complex physical systems by selecting sparse combinations of candidate functions. Instead of fitting a purely predictive machine learning model, PySINDy attempts to recover interpretable differential equations that explain how a system evolves over time. ... -
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Build production-grade data and ML workflows, hassle-free The infinitely scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks. Don’t let friction between development and production slow down the deployment of new data/ML workflows and cause an increase in production bugs. Flyte enables rapid experimentation with production-grade software. Debug in the cloud by iterating on the workflows locally to achieve tighter feedback loops. As your...
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Ragas
Supercharge Your LLM Application Evaluations
Objective metrics, intelligent test generation, and data-driven insights for LLM apps. Ragas is your ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. Say goodbye to time-consuming, subjective assessments and hello to data-driven, efficient evaluation workflows. Don't have a test dataset ready? We also do production-aligned test set generation. -
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LlamaParse
Parse files for optimal RAG
LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers.
