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import streamlit as st
def run():
st.title("6. Deployment & Testing")
st.header("Introduction")
st.write("""
Model Deployment is the process of integrating a machine learning model into a production environment where it can make predictions on new data.
""")
st.header("Objectives")
st.write("""
- Integrate the model into production.
- Monitor model performance.
- Update the model as needed.
""")
st.write("## Overview")
st.write("Deploying the model and testing its real-world performance.")
st.write("## Key Concepts & Explanations")
st.markdown("""
- **Deployment**: Making the model available for use (e.g., via an API).
- **Testing**: Ensuring the model works in production environments.
- **Model Monitoring**: Continuously tracking model performance in real-time.
""")
st.write("## Quiz: Conceptual Questions")
q1 = st.radio("Which of the following is part of deployment?", ["Model Training", "Model Versioning", "Model Testing"])
if q1 == "Model Versioning":
st.success("✅ Correct!")
else:
st.error("❌ Incorrect.")
st.write("## Code-Based Quiz")
code_input = st.text_area("Write code to save a model using joblib", value="import joblib\njoblib.dump(model, 'model.pkl')")
if "joblib.dump" in code_input:
st.success("✅ Correct!")
else:
st.error("❌ Try again.")
st.write("## Learning Resources")
st.markdown("""
- 📘 [Machine Learning Model Deployment](https://towardsdatascience.com/deploying-machine-learning-models-using-flask-285dbddedbfa)
""")
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