ml-production

Azure Databricks MLOps sample for Python based source code using MLflow without using MLflow Project.

A "production-ready" simple project template to quickly start an Artificial Intelligence (AI), Machine Learning (ML) and/or Data Science (DS) project with basic files, branches and directory structure.

Coursera Machine Learning Engineering for Production Specialization Course

Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]

Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production

Website built in JavaScript & React as a "blog" to document an ML pipeline I built for Apartment Price Scraping project

Data Versioning with DVC demonstrates how to use Data Version Control (DVC) to efficiently track and manage datasets for machine learning projects while maintaining reproducibility and keeping code and data versions synchronized.

Reference implementation for deploying ML models from notebooks to production

MLOps End-to-End Vehicle Insurance Project is a production-style machine learning pipeline for vehicle insurance data, covering data ingestion, validation, transformation, model training, evaluation, deployment, and CI/CD using MongoDB, AWS, Docker, and GitHub Actions.