Search Results for "as built"
Sort By:
Showing 124 open source projects for "as built"
View related business solutions-
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
1
YOLOv5
YOLOv5 is the world's most loved vision AI
Introducing Ultralytics YOLOv8, the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. ... -
2
Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? -
3
Pedalboard
A Python library for audio
...It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, pedalboard is used for data augmentation to improve machine learning models and to help power features like Spotify’s AI DJ and AI Voice Translation. pedalboard also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation. -
4
scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
5
X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
...It supports labeling tasks across images and videos and enables developers to prepare training datasets for tasks such as object detection, segmentation, classification, tracking, and pose estimation. 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. -
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. NVIDIA FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment. -
7
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. -
8
Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. ... -
9
mlforecast
Scalable machine learning for time series forecasting
...It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
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.
-
10
Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. ... -
11
TNT
A lightweight library for PyTorch training tools and utilities
...This design helps separate concerns such as model training, evaluation, logging, and checkpointing, making machine learning experiments easier to manage. The framework is particularly useful for large-scale experiments where maintaining clear training workflows becomes increasingly important. Because it is built on top of PyTorch, the framework integrates naturally with existing deep learning models and datasets. -
12
mosaicml composer
Supercharge Your Model Training
...It includes built-in support for distributed training strategies such as Fully Sharded Data Parallelism and standard Distributed Data Parallel execution, helping teams scale models without having to assemble as much infrastructure by hand. -
13
TorchRec
Pytorch domain library for recommendation systems
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. -
14
Humanoid-Gym
Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real
Humanoid-Gym is a reinforcement learning framework designed to train locomotion and control policies for humanoid robots using high-performance simulation environments. The system is built on top of NVIDIA Isaac Gym, which allows large-scale parallel simulation of robotic environments directly on GPU hardware. Its primary goal is to enable efficient training of humanoid robots in simulation while enabling policies to transfer effectively to real-world hardware without additional training. The framework emphasizes the concept of zero-shot sim-to-real transfer, meaning that behaviors learned in simulation can be deployed directly on physical robots with minimal adjustment. ... -
15
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... -
16
PyTorch Forecasting
Time series forecasting with PyTorch
...A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. -
17
zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt. The core concept of the system is visual, and the name of the interface corresponds to it one-to-one, so it is also uniform and extensible. You can write and run the strategy in your favorite ide, and then view its related targets, factor, signal and performance on the UI. ... -
18
PyTorch Ignite
Library to help with training and evaluating neural networks
...Thus, we do not require to inherit from an interface and override its abstract methods which could unnecessarily bulk up your code and its complexity. Extremely simple engine and event system. Out-of-the-box metrics to easily evaluate models. Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics. -
19
Karpathy
An agentic Machine Learning Engineer
karpathy is an experimental agentic machine learning engineer framework designed to automate many aspects of the ML development workflow. The project sets up a sandboxed environment where an AI agent can access datasets, run experiments, and generate machine learning artifacts through a web interface. Its startup script automatically prepares the environment by creating a sandbox directory, installing key ML libraries, and launching the agent interface. The system is tightly integrated with... -
20
NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
21
RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline.... -
22
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. A Gradio interface can automatically generate a public link you can share with colleagues that... -
23
SpikingJelly
SpikingJelly is an open-source deep learning framework
SpikingJelly is an open-source deep learning framework for spiking neural networks that is primarily built on top of PyTorch and aimed at neuromorphic computing research. The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. This makes it especially relevant for researchers interested in biologically inspired computing, event-driven processing, and energy-efficient AI systems. ... -
24
lightning AI
The most intuitive, flexible, way for researchers to build models
...Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. ... -
25
pmdarima
Statistical library designed to fill the void in Python's time series
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
