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 + 12 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 comprehensive solution for monitoring your AI models in production
A robust (🐢) and fast (🐇) MLOps tool for managing data and pipelines in Rust (🦀)
AI-Powered Financial Asset Forecasting: Predicts long-term US stock prices using AI. Integrates news sentiment, technical indicators, candlestick patterns, and fundamental analysis. Provides comprehensive insights for informed financial decision-making. Customizable data collection and analysis for investors and researchers.
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
A Helm chart containing Kubeflow Pipelines as a standalone service.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
This repository contains examples of using various libraries/tools for MLOps.
Here you will find a selection of miscellaneous data science projects that are not included in my project portfolio.
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
End-to-end ML project for tabular data.
Advise one of Cognizant’s clients on a supply chain issue by applying knowledge of machine learning models.
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