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This repository is the solution for the iFood ML Engineer test.
Home Page: https://github.com/andre-marcos-perez/ifood-ml-readme
License: MIT License
Python 42.11%
Shell 4.85%
Dockerfile 5.25%
Jupyter Notebook 47.78%
ifood-mlops-app's Introduction
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- Article: Machine Learning Models from the Engineering Perspective - Data Team Stone on Medium - 🇧🇷 - March, 2021.
- Article: About the history of distributed computing and Kubernetes clusters - Data Team Stone on Medium - 🇧🇷 - January, 2021.
- Article: Getting started with MongoDB, PySpark, and Jupyter Notebook (mention) - MongoDB Tech Blog - 🇬🇧 - October, 2020.
- Article: Apache Spark Cluster on Docker (ft. a JupyterLab Interface) - Towards Data Science on Mediume - 🇬🇧 - July, 2020;
- Paper: Risk Management in E-Commerce - A Fraud Study Case Using Acoustic Analysis - MDPI Entropy - 🇬🇧 - November, 2019;
- Podcast: Data, IA and ML in our daily lives - DevPira podcast - 🇧🇷 - May, 2021.
- Podcast: Data Engineering at Experian - Hipsters.Tech podcast - 🇧🇷 - July, 2020.
- Talk: Feature Store Cookbook - The Developer's Conference - 🇧🇷 - June, 2021.
- Talk: The Mindset and Elements of a Machine Learning Platform - The Developer's Conference - 🇧🇷 - March, 2021.
- Live: Meet Stone's Credit Data Team - EstatiDados YouTube Channel - 🇧🇷 - March, 2021.
- Live: Building a Data Lakes on AWS - EstatiDados YouTube Channel - 🇧🇷 - December, 2020;
- Course: Data Science - EBAC - 🇧🇷 - April, 2021;
- Course: Data Analyst - EBAC - 🇧🇷 - April, 2021;
- Course: SQL for Data Analysis - EBAC - 🇧🇷 - April, 2021;
- Course: Python for Data Analysis - EBAC - 🇧🇷 - April, 2021.
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