There are 0 repository under ml-deployment topic.
Management Dashboard for Torchserve
PMML scoring library for Scala
An end-to-end Machine Learning project from writing a Jupyter notebook to check the viability of the solution, to breaking down the same into modular code, creating a Flask web app integrated with a HTML template to make a website interface, and deploying on AWS and Azure.
Pushing Deep Learning models into production using torchserve, kubernetes and react web app :smile:
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Simply Automate Monitoring Infrastructure with Terraform, Ansible, AWS EC2, Nginx, Prometheus, Grafana and Github Actions :smile:
Identifying Patterns and Trends in Campus Placement Data using Machine Learning
A EKS-based ML deployment solution
This repo shows how to implement a simple image generation app that uses Jax-Implementation of a conditional VAE, Jax, fastapi, docker, streamlit, heroku, ec2, and cloudflare :smiley:
An implementation of seminal CVPR 2016 paper: "A Hierarchical Deep Temporal Model for Group Activity Recognition."
An ECS-based ML deployment solution
Powerful AutoML toolkit
Base classes and utilities that are useful for deploying ML models.
This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.
A regression model to predict calories burnt using values from multiple sensors.
A basic example of deploying machine learning applications
ml-deploy-lite is a Python library designed to simplify the deployment of machine learning models. It allows developers to quickly turn their models into REST APIs or gRPC services with minimal configuration. The library integrates seamlessly with Docker and Kubernetes, providing built-in monitoring and logging for performance and error tracking.
Ensemble Learning | Flask
This is Mudit Vyas worked as ML Developer Intern , Team Leader in Technocolabs Software.This is Internship Project For Technocolabs Software.
Demonstration of building a machine learning model and deploying it on a web app.
A web app to showcase some of my favorite projects
:globe_with_meridians: Language identification for Scandinavian languages
A conversational AI assistant designed to provide users with reliable, non-diagnostic medical information, appointment scheduling support, symptom checking guidance, and personalized wellness tips.
A simple and effective machine learning project that predicts salaries based on years of experience using linear regression. Includes data preprocessing, model training, evaluation, and deployment via Streamlit.
🌸 IrisPredictor is a smart Django web app that uses 🧠 machine learning to predict 🌺 Iris flower species based on petal and sepal features. Simple, fast, and beautiful!
AI-powered customer churn prediction web app using Django & Machine Learning. Includes form-based prediction, result visualization, and EDA dashboard.
A Streamlit-based churn prediction app using a trained Random Forest model to analyze customer behavior and predict churn based on demographics, spending, interaction history, and service usage.
Predicts risk of heart disease (Yes/No) using top medical indicators — powered by Logistic Regression.
A full-stack machine learning architecture for food delivery ETA prediction, leveraging a DVC-driven pipeline, automated CI/CD workflows, cloud artifact management, and LGBM-based stacked regression ensemble for high-fidelity time estimations.
Predicting global happiness scores given factors like GDP, social support, freedom...etc.
Predicting the price of an apartment