Code and files to go along with CS329s machine learning model deployment tutorial.
2018年国际AIOps挑战赛KPI时序异常检测比赛基于OpenMLDB部署的工程化部署实践方案
Admission Prediction website in US elite colleges. This website uses a Machine Learning model trained using Linear Regression technique. Website takes score as input from users to predict the results based on previously trained Machine Learning model.
Machine learning project that predicts whether an email is spam or not.
The model features on the system are all handled by hand gestures. A deep-learning model is used to track the hand and fingers. The tracked fingers then will be made use to generate the click signs and access the other functionalities of the system.
Come and check your chances of surviving the titanic in this web app.
The Titanic StreamLit Website is an interactive web platform showcasing machine learning models developed for the Kaggle Titanic dataset. The website features a homepage and dedicated pages for Neural Network, Random Forest, and Gradient Boosted Trees models. This project serves as a testament to the deployment of machine learning models.
This repository is an implementation of running python flask app on docker environment. On this project we will detect apples, bananas, and oranges using Yolov5 custom model, and then classify that using Tensorflow custom model. It can be done using image link.
Get Powerful quotes on your phone or pc!!! NLP WEB APP
Django projects
Image classification model deployment to GCP. Using Streamlit for an interface.
A curated collection of AI-powered applications for a versatile and intelligent user experience of Web and Mobile Apps.
[Talk] "Become a Data Storyteller with Streamlit" | 🇨🇿 PyData Prague'23 & 🇩🇪 PyMunich'24
A Flask Web App For Diabetes Prediction
A simple machine learning web-based app using flask python
Source code of team 4 for hacktiv8 Thunder talk.
Deploying a clothing classification tensorflow model to an API using tflite, Docker, AWS Lambda and Gateway
In this project, CI/CD pipeline is being created using GitHub Action and Azure DevOps organization
Data analysis and ML Modelling
An end-to-end ML model deployment pipeline on GCP: train in Cloud Shell, containerize with Docker, push to Artifact Registry, deploy on GKE, and build a basic frontend to interact through exposed endpoints. This showcases the benefits of containerized deployments, centralized image management, and automated orchestration using GCP tools.
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