Launch an MLFlow server through Docker

ML project using Pytorch & Tensorboard with Github Actions for CI/CD. Runs a Training job on local machine / A Github runner using github actions workflow

This project deploys a diabetes prediction model on AWS using MLOps principles. It features a Flask-based UI for user interaction and utilizes CI/CD pipelines for automated deployment. By leveraging AWS infrastructure, the project ensures scalability, version control, and monitoring of the deployed model.

This contains the dvc files created from data versioning.

'Roiergasias' kubernetes operator is meant to address a fundamental requirement of any data science / machine learning project running their pipelines on Kubernetes - which is to quickly provision a declarative data pipeline (on demand) for their various project needs using simple kubectl commands. Basically, implementing the concept of No Ops. …

Testing and implementations with ClearML

End-to-end MLOps Using MLflow for ML lifecycle, including data validation, processing, model training, evaluation, validation and deployment

An application for violent threat detection

A simple microservice application to track machine learning experiments

Some examples of running R in a Docker container with machine learning and MLOps features

The project's goal is to predict failures due to sensors in Air Pressure System and reduce the failures , as well as to save money on unnecessary repairs due to that failures.

I constructed a machine learning model to predict the quality of wine

Testing deployment of PyMC models using MLFlow and BentoML.

batch data pipeline

A blog where I siddha-artha(Sankrit for prove meanings) about ML/AI and coding

In this project, the system in focus is the Air Pressure system (APS) which generates pressurized air that are utilized in various functions in a truck, such as braking and gear changes. The datasets positive class corresponds to component failures for a specific component of the APS system.

A basic project structure for machine learning projects with MLflow