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.
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Updated
Jun 13, 2024 - Python
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.
Machine Learning Engineering Open Book
😎 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
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. 📊📈📉👨💻
Tutorials on how to engineer Machine Learning projects using Deep Neural Networks with PyTorch and Python
My repo for the Machine Learning Engineering bootcamp 2022 by DataTalks.Club
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.
Capstone project for Udacity
A Helm chart containing Kubeflow Pipelines as a standalone service.
This repository contains examples of using various libraries/tools for MLOps.
Having fun with MLOPS: Wine Stuff
Here you will find a selection of miscellaneous data science projects that are not included in my project portfolio.
My professional resume
Develop a single endpoint to predict the sales of a company
This project aims to apply MLOps techniques to deploy a machine learning model through an API constructed with FastAPI. We utilize Poetry for dependency management and Docker for containerization, ensuring the code is modular, organized 📐, and maintainable 🛠️.
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