Search Results for "management framework"
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Showing 23 open source projects for "management framework"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed... -
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Rasa
Open source machine learning framework to automate text conversations
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. -
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. -
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skfolio
Python library for portfolio optimization built on top of scikit-learn
skfolio is a Python library designed for portfolio optimization and financial risk management that integrates closely with the scikit-learn ecosystem. The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps... -
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Qlib
Qlib is an AI-oriented quantitative investment platform
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. With Qlib, users can easily try their ideas to create better Quant investment strategies. At the module level, Qlib is a platform that consists of... -
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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 both experimentation and production-scale development. ... -
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C3
The goal of CLAIMED is to enable low-code/no-code rapid prototyping
C3 is an open-source framework designed to simplify the development and deployment of data science and machine learning workflows through reusable components and low-code development techniques. The framework focuses on enabling rapid prototyping while maintaining a path to production through automated CI/CD integration. CLAIMED provides a component-based architecture where data processing steps, models, and workflows can be packaged into reusable operators. These operators can be... -
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Ploomber
The fastest way to build data pipelines
Ploomber is an open-source framework designed to simplify the development and deployment of data science and machine learning pipelines. It allows developers to transform exploratory data analysis workflows into production-ready pipelines without rewriting large portions of code. The system integrates with common development environments such as Jupyter Notebook, VS Code, and PyCharm, enabling data scientists to continue working with familiar tools while building scalable workflows. Ploomber... -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and... -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
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SkyPilot
SkyPilot: Run AI and batch jobs on any infra
SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability. Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. ... -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used... -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend.... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
...E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. ... -
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LUMINOTH
Deep Learning toolkit for Computer Vision
...The toolkit provides command-line utilities for dataset management, training, and inference, making it easier to integrate into research workflows and production systems. Although the project is no longer actively maintained, it remains a useful educational and experimental platform for studying object detection pipelines and deep learning workflows. -
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pySPACE
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due... -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection -...
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