Machine Learning Engineering Open Book

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

😎 A curated list of awesome MLOps tools

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 11 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng

Frouros: an open-source Python library for drift detection in machine learning systems.

💻 Decoding ML articles hub: Hands-on articles with code on production-grade ML

A framework for forecasting stock prices with emphasis on Machine Learning best practices.

Tutorials on how to engineer Machine Learning projects using Deep Neural Networks with PyTorch and Python

A robust (🐢) and fast (🐇) MLOps tool for managing data and pipelines in Rust (🦀)

My repo for the Machine Learning Engineering bootcamp 2022 by DataTalks.Club

A Helm chart containing Kubeflow Pipelines as a standalone service.

This repository contains examples of using various libraries/tools for MLOps.

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

The tasks I was required to complete as a part of the BCG Open-Access Data Science & Advanced Analytics Virtual Experience Program are all contained in this repository. 📊📈📉👨‍💻

Machine Learning for Production Specialization

Flower Classification Web Application (Built with Flask)

Here you will find a selection of miscellaneous data science projects that are not included in my project portfolio.

Repository showcasing my Machine Learning Engineering Apprenticeship at AXA-Direct Assurance, contributing to the development and implementation of Machine Learning solutions.

Leverage Metaflow, PyTorch, AWS S3, Elasticsearch, FastAPI and Docker to create a production-ready facial recognition solution. It demonstrates the practical use of deep metric learning to recognize previously unseen faces without prior training.