Provides end-to-end model development pipelines for LLMs and Multimodal models that can be launched on-prem or cloud-native.

pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.

A universal orchestration workbench for macOS. Seamlessly mix AI models (Codex, Claude) with traditional CLI tools (git, npm) via static DAG pipelines or dynamic self-healing agents, creating a fully automated closed-loop for development and ops.

Visual AI development framework for training and inference of ML models, scaling pipelines, and automating workflows with Python

This repository demonstrates an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.

A framework for node based tools including a graph data model, a UI foundation and graph-to-asset pipeline. Use this package to speed up the development of graph based tools for the Unity Editor that adhere to Unity UI and UX guidelines. 📦 [Mirrored from UPM, not affiliated with Unity Technologies.]

The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.

Pipeline module for parallel real-time data processing for machine learning models development and production purposes.

Outbound PSTN calling agent using LiveKit SIP trunks with a voice pipeline (Silero VAD, Deepgram STT, OpenAI LLM+TTS). Includes a simple CLI for local development, health checks, and model prewarming.

An analytic workbench for user-guided development of model pipelines

RKI Metadata Exchange | Software development toolkit for the MEx project that contains utilities for building pipelines and services, including shared code for logging, configuration and data models.

Our goal with this ML pipeline template is to create a user friendly utility to drastically speed up the development and implementation of a machine learning model for all sorts of various problems.

A demo to illustrate a basic 3D model content creation pipeline and the six fundamentals of HoloLens development; gaze, gesture, voice, spatial mapping, spatial sound and world coordinates.

Repo of repeatable ML model pipeline development and deployment

This repository provides a complete pipeline for non-invasive blood glucose estimation using Photoplethysmography (PPG) signals. It includes data preprocessing, feature extraction, machine learning model training, and result visualization to support research and development in biomedical signal analysis and diabetes screening.

Pipeline for prediction of monomer glass transition temperature, and development of QSAR/QSPR models via mol2vec

This is a sign language gamification mobile development system that integrates Unity for game mechanics, Blender for 3D character modeling and animation, and C# for game logic and features a structured CI/CD pipeline using GameCI for seamless deployment

Machine Learning Operations (MLOps) are essential to build successful Data Science use-cases. Today, ML is powering data driven use-cases that are transforming industries around the world. In order to seize and hold it's competitive advantage business needs to reduce risk therefore a new expertise rises to include data science models in operational systems. According to Gartner Research “While many organizations have experimented with AI proofs of concept, there are still major blockers to operationalizing its development. IT leaders must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. (July 2020)”. In this session, we will discuss the role of MLOps and how they can help data science models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.

Here is the documentation about the construction and methods used in medical image classification with MRI and 3D convolutional nueral networks. It's important to you'd been prior expirience with neuroscience software as ANTs, SPM12, FREESURFER, FSL or even Nipype among others. As a general view the project has 3 stages: Preprocessing pipeline development, model construction and trainning and interpretability analysis

MyMoviePlan Project 4 DESCRIPTION Create a dynamic and responsive web application for booking movie tickets online for different genres and languages. Background of the problem statement: NMS Cinemas is a chain of single screen theatres that screen movie shows of different genres and languages at very genuine prices. It was established in 2004 in Pune, India. Recently, the business analysts noticed a decline in sales since 2010. They found out that the online booking of movie tickets from apps, such as BookMyShow and Paytm were gaining more profit by eliminating middlemen from the equation. As a result, the team decided to hire a Full Stack developer to develop an online movie ticket booking web application with a rich and user-friendly interface. You are hired as the Full Stack Java developer and are asked to develop the web application. The management team has provided you with the requirements and their business model so that you can easily arrange different components of the application. Features of the application: Registration Login Payment gateway Searching Filtering Sorting Dynamic data Responsive and compatible with different devices Recommended technologies: Database management: MySQL and Oracle Backend logic: Java programming, NodeJS Frontend development: JSP, Angular, Bootstrap, HTML/CSS, and Javascript Automation and testing technologies: Selenium, Jasmine, and TestNG DevOps and production technologies: Git, GitHub, Jenkins, Docker, Kubernetes, and AWS Project development guidelines: The project will be delivered within four sprints with every sprint delivering a minimal viable product. It is mandatory to perform proper sprint planning with user stories to develop all the components of the project. The learner can use any technology from the above-mentioned technologies for different layers of the project. The web application should be responsive and should fetch or send data dynamically without hardcoded values. The learner must maintain the version of the application over GitHub and every new change should be sent to the repository. The learner must implement a CI/CD pipeline using Jenkins. The learner should also deploy and host the application on an AWS EC2 instance. The learner should also implement automation testing before the application enters the CI/CD pipeline. The learner should use Git branching to do basic automation testing of the application in it separately. The learner should make a rich frontend of the application, which is user- friendly and easy for the user to navigate through the application. There will be two portals in the application, namely admin and user portal. Admin Portal: It deals with all the backend data generation and product information. The admin user should be able to: Add or remove different genres to or from the application to build a rich product line Edit movie details like name, ticket price, language, description, and show timings to keep it aligned to the current prices Enable or disable the movie shows from the application User Portal: It deals with the user activities. The end-user should be able to: Sign-in to the application to maintain a record of activities Search for movie tickets based on the search keyword Apply filters and sort results based on different genres Add all the selected movie tickets to a cart and customize the purchase at the end Experience a seamless payment process Receive a booking summary page once the payment is complete

An intelligent, multi-agent system to autonomously plan, write, edit, fact-check, and publish a complete book using the CrewAI framework and Groq's LLaMA-3-70B model. This AI pipeline mimics the collaborative process of human authors, editors, and publishers to streamline creative book development.

The ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

The ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting departure runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

This project is an end-to-end machine learning pipeline with a focus on efficient model deployment using Flask API, Docker, and Amazon EC2. The modular architecture ensures seamless integration and a consistent experience across environments. A CI/CD pipeline with GitHub Actions streamlines development and deployment.

Unlearn Diff is an open-source Python package designed to streamline the development of unlearning algorithms and establish a standardized evaluation pipeline for diffusion models. It provides researchers and practitioners with tools to implement, evaluate, and extend unlearning algorithms effectively.

Data Engineering Nanodegree projects and exercises, including Data Modeling, Data Warehousing, Data Lake development, and Pipeline Management.

claude-symphony is a 10-stage software development workflow pipeline that orchestrates multiple AI models (Claude, Gemini, Codex) like an orchestra, creating harmonious software through a complete development cycle.