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

ARIMA time series model with end-to-end mlops using Google Cloud Platform (GCP).

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.

Declarative Jenkins pipelines for python based project

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

Testing deployment of PyMC models using MLFlow and BentoML.

batch data pipeline

Equipo 3 - Arquitectura de Producto de Datos

Custom versioning of Machine Learning models