🔍 Deploying Machine Learning Models
This lecture, part of the ML-Zoomcap series, is about bridging the gap between model development and production. We covered:
🧩 Topics include:
<ol>
<li>Saving &amp; Loading Models – Make your trained model easily reusable.</li>
<li>Web Services – Intro to Flask to serve your model in a web app.</li>
<li>Virtual Environments – Using Pipenv for clean package management.</li>
<li>Docker for Environment Management – Streamlining deployment across platforms.</li>
<li>Cloud Deployment – Launching on AWS Elastic Beanstalk (plus other free options)</li>
</ol>
I also completed the Week 5 homework! 🎓 Feel free to check out my solutions and code here: <a href="https://github.com/RuiFSP/ml-zoomcamp/blob/main/05-deployment/homework05/homework05.ipynb" target="_blank" rel="noopener nofollow">homework05</a>
On to week 6! 💪
#MachineLearning #DataScience #MLDeployment #Flask #Docker #AWS #MLZoomcamp

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LearningInPublic

The fifth week of the Machine Learning Zoomcamp focused on deploying machine learning models. Key topics covered include saving and loading models, serving models using Flask, managing packages with Pipenv, deploying with Docker, and cloud deployment through AWS Elastic Beanstalk. Homework solutions are also available for review.

🚀 Week 5 of Machine Learning Zoomcamp Complete!🎉