Harsh188/100-Days-of-ML-Pt2
100 Day ML Challenge to learn and develop machine learning products. Since this is my second time performing this challenge, this time around I will be focusing more on the production enviroment rather than the concepts and theory behind ML/DL models. I will be placing heavy emphasis on the ML pipeline and the process of taking an ML model and applying into a real-world application.
This project offers a daily log and practical guide for machine learning engineers aiming to deploy ML models into production environments. It focuses on the end-to-end ML pipeline, emphasizing practical application over theoretical concepts. The project documents the process of taking a machine learning model, such as a linear regression or deep learning model, and integrating it into real-world applications using tools like Flask, Streamlit, Docker, and Kubernetes.
No commits in the last 6 months.
Use this if you are a machine learning engineer or MLOps practitioner looking for a structured, hands-on approach to learn and implement ML model deployment and pipeline automation.
Not ideal if you are new to machine learning theory or deep learning concepts and are looking for an introduction to the fundamentals.
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1
Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 19, 2021
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