Build & Deploy AI/ML Web Apps: Hands-On Tutorial (Streamlit,GitHub, API)
👨🏫 In this hands-on machine learning session, Dr. Onoja guides students through the complete end-to-end workflow of deploying an ML model using Python — from training in Jupyter Notebook to publishing on Streamlit Cloud.
🧠 What You’ll Learn:
0:00 – 7:00 | How to train a Random Forest classifier on the Iris dataset
8:00 – 24:00 | How to use Generative AI (e.g., ChatGPT/Copilot) to write production-ready Python scripts
31:00 – 47:20 | How to upload your project to a GitHub repository
47:20 – 49:50 | How to deploy a fully functional Streamlit app to the web
🎯 Key Skills Practiced:
Model training & evaluation
Serialization with Joblib
Prompting GenAI for boilerplate code
GitHub repository creation & commit workflow
Model deployment using Streamlit cloud
🧰 Tools Used:
Python, Jupyter Notebook, scikit-learn, Streamlit, GitHub, Generative AI (ChatGPT/Copilot)
📦 Dataset:
The classic Iris dataset — ideal for demonstrating classification tasks.
📍 Recorded live during a DataEdge Academy class session on model deployment.
📌 Whether you're a data science student or an aspiring ML engineer, this session offers real-world exposure to production-ready workflows.
🌐 Helpful Links:
Github repo: https://github.com/Donmaston09/explai...
🔗 Streamlit: https://streamlit.io/
🚀 Live App: Deployed Iris Classifier
🔗 Connect:
📧 Email: donmaston09@gmail.com
📺 YouTube: @tonyonoja7880
🌐 Facebook: DataEdge Academy
👍 Don’t forget to Like, Subscribe, and Comment if you found this helpful!
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