AI Makerspace: Blueprints for developing machine learning applications with state-of-the-art technologies.

Example project with a complete MLOps cycle: versioning data, generating reports on pull requests and deploying the model on releases with DVC and CML using Github Actions and IBM Watson. Part of the Engineering Final Project @ Insper

AIOps-project-DVC-NLP-usecase

This project contains the production ready Machine Learning(Deep Learning) solution for detecting and classifying the brain tumor in medical images

This project aims to classify chicken fecal samples into two categories: diseased (Coccidiosis) and healthy. The classification is based on analyzing images of the fecal samples using computer vision techniques.

Clustering-based ML on the stock dataset using Kmeans, DVC, and MLflow

Testing MLOps approach to predict houses' prices in Saint-Petersburg

DVC Monorepo Designs for Multiple Pipelines

This project contains the production-ready Machine Learning solution for detecting and classifying Covid-19, Viral disease, and No disease in posteroanterior and anteroposterior views of chest x-ray

Census data classification model with Random Forest Classifier

"End-to-End Machine Learning Pipeline Creation Using DVC: A comprehensive MLOps solution on GitHub." This GitHub repository showcases the implementation of an end-to-end machine learning pipeline using DVC (Data Version Control) for efficient data management and MLOps practices. The pipeline covers the entire machine learning workflow.

This project focuses on forecasting customer churn in the telecom industry by leveraging various features. The goal is to implement a straightforward, real-time prediction system capable of handling both batch and online predictions. The predictive model is deployed using Streamlit, providing an interactive and user-friendly interface for exploring

GAN based Super resolution media engine

A comprehensive MLOps structure that includes the use of CI/CD pipelines, DVC, MLflow, Git workflow, and Heroku. This structure covers the complete lifecycle of machine learning operations, from continuous integration and deployment pipelines to version control with DVC, experiment tracking with MLflow, collaborative development with Git workflow,

An example of DVC pipeline with a Docker-wrapped command

End to End Machine Learning MLOps Project for Credit Card Fraud Detection using Ensemble Models, Data and Model Versioning through DVC, Github Actions, and Deployment

This project contains the production ready Machine Learning solution to make the prediction on the batch of data coming from Air Pressure system (APS) sensors

Skeleton for DVC pipeline to evaluate multiple models together

This is a test project to learn how to use pytorch (pytorch-lightning) for computer vision and how to include DVC and MLflow in my workflow

Implementation of USAD (UnSupervised Anomaly Detection on multivariate time series) in PyTorch Lightning