Host MLFlow Tracking Server and Model Registry as a containerized application on Kubernetes

Automating machine learning experiment tracking with MLFlow on AWS and Dagshub.

Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow

Small test to see how MLFLOW relates to experiment tracking with Streamlit

CLASSIFY DOGS AND CATS IMAGES AND BUILT A WEB APP FOR THE SAME

A custom model that uses ResNet 50 for feature extraction to detect bone fractures from a patient's X-ray.

A minimum Python package built on top of the LangChain framework to interact with LLM.

Mlflow Proxy-Authorization plugin

Production Level MLOps Project for Titanic Dataset

Advanced predictive model for box office revenue. With precision forecasting and confidence-building insights, our solution empowers production houses to optimize resources and maximize profitability.

🌎 🚙📚 Predicting travel times and traffic density on a highway in Slovenia

Implementation of MLops pipeline for Chest Disease Classification from Chest CT Scan Images using computer vision Vgg16 pretrained Image classification model. further perform deployment on AWS EC2 using Docker, CI/CD Jenkins tool, using Flask as front end interface.

This is an end-to-end animal face classification model with Keras, KerasTuner, Mlflow, SQLite, Streamlit, and FastAPI which can classify animal faces as either cat, dog or wildlife

Testing deployment of PyMC models using MLFlow and BentoML.

TechCon Experimentation with MLFlow and Dask

Run tidyverse, tidymodels, targets, carrier, and MLFlow within Docker

A Machine Learning project to predict the success or failure of startups based on data by using ensemble modeling techniques, MLflow for tracking experiments, Docker for containerization.

Predicting London's climate using machine learning techniques. This project aims to forecast mean temperature in Celsius (°C) using various regression models and logging experiments with MLflow

This project focuses on predicting department-wide sales for each store for the upcoming year while also considering the impact of markdowns during holiday weeks. The goal is to provide valuable insights to assist in decision-making and offer recommended actions to maximize business impact.