Search Results for "machine learning platform" - Page 2
Sort By:
Showing 1054 open source projects for "machine learning platform"
View related business solutions-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
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.
-
1
Coursera-ML-AndrewNg-Notes
Personal notes from Wu Enda's machine learning course
Coursera-ML-AndrewNg-Notes is an open-source repository that provides detailed study notes and explanations for Andrew Ng’s well-known machine learning course. The project aims to help students understand the mathematical concepts, algorithms, and intuition behind fundamental machine learning techniques taught in the course. It organizes the material into clear written summaries that accompany each lecture topic, including supervised learning, regression methods, neural networks, and optimization algorithms. ... -
2
X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
...The tool is built with an interactive graphical interface that simplifies annotation workflows and allows users to draw and edit labels directly on visual data. It also supports a wide range of export formats compatible with popular machine learning pipelines, making it easier to integrate with training frameworks. -
3
machine_learning_examples
A collection of machine learning examples and tutorials
machine_learning_examples is an open-source repository that provides a large collection of machine learning tutorials and practical code examples. The project aims to teach machine learning concepts through hands-on programming rather than purely theoretical explanations. It includes implementations of many machine learning algorithms and neural network architectures using Python and popular libraries such as TensorFlow and NumPy. -
4
Orion
A machine learning library for detecting anomalies in signals
Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. -
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
5
ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training methods, and common algorithmic techniques. ... -
6
NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. -
7
TabPFN
Foundation Model for Tabular Data
TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. ... -
8
BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
9
Cube Studio
Cube Studio open source cloud native one-stop machine learning
Cube Studio is an open-source, cloud-native end-to-end machine learning and AI platform designed to support the full lifecycle of AI development — from data preparation and interactive notebook coding to distributed training, model tuning, and deployment in production-ready environments. It provides a unified interface where teams can manage data sources, track datasets, and build pipelines using drag-and-drop workflow orchestration, making it accessible for both engineers and data scientists working at scale. ... -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
10
Flower
Flower: A Friendly Federated Learning Framework
...Many components can be extended and overridden to build new state-of-the-art systems. Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand. -
11
Keras
Python-based neural networks API
Python Deep Learning library -
12
River ML
Online machine learning in Python
River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow. -
13
deepfakes_faceswap
Deepfakes Software For All
Faceswap is the leading free and open source multi-platform deepfakes software. When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. -
14
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. -
15
Key-book
Proofs, cases, concept supplements, and reference explanations
The book "Introduction to Machine Learning Theory" (hereinafter referred to as "Introduction") written by Zhou Zhihua, Wang Wei, Gao Wei, and other teachers fills the regret of the lack of introductory works on machine learning theory in China. This book attempts to provide an introductory guide for readers interested in learning machine learning theory and researching machine learning theory in an easy-to-understand language. ... -
16
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... -
17
ComfyUI-3D-Pack
An extensive node suite that enables ComfyUI to process 3D inputs
ComfyUI-3D-Pack is an extension package for the ComfyUI visual AI workflow environment that enables users to generate and manipulate 3D assets using advanced machine learning techniques. ComfyUI itself is a node-based interface for designing and executing generative AI pipelines, and this extension expands its capabilities by introducing nodes specifically designed for working with three-dimensional data. The package allows the platform to process inputs such as meshes and UV textures and integrate them into generative workflows similar to those used for image and video generation. ... -
18
AmpliGraph
Python library for Representation Learning on Knowledge Graphs
Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs. -
19
SageMaker Python SDK
Training and deploying machine learning models on Amazon SageMaker
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. -
20
TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
21
Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. -
22
cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
...The resource is particularly useful for candidates preparing for technical interviews in data science, machine learning, or analytics roles. -
23
EconML
Python Package for ML-Based Heterogeneous Treatment Effects Estimation
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. ... -
24
AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. ... -
25
Newton
An open-source, GPU-accelerated physics simulation engine
Newton is a high-performance, GPU-accelerated physics simulation engine designed primarily for robotics research, machine learning, and advanced simulation workflows. Built on top of NVIDIA Warp, it leverages GPU parallelism to deliver scalable and efficient simulation environments that support rapid iteration and experimentation. The engine extends previous simulation frameworks by introducing differentiable physics capabilities, allowing it to integrate seamlessly with machine learning models and optimization pipelines. ...
