Assignment: Build and Deploy a Complete Low-Code ML Application

Phase 17: Low-Code AI Tools
Total Points: 100
Due: As specified by your instructor

📋 Assignment Overview

Build and deploy a complete machine learning application using low-code tools. Your application should include AutoML model training, an interactive interface (Gradio or Streamlit), and deployment to Hugging Face Spaces.

🎯 Learning Objectives

  • Apply AutoML for model development

  • Build user-friendly ML interfaces

  • Deploy applications to the cloud

  • Integrate multiple low-code tools

  • Follow best practices for production ML

📝 Requirements

Part 1: Dataset and Problem (15 points)

Choose ONE of the following:

Option A: Classification Problem

  • Predict customer behavior (churn, purchase, click)

  • Medical diagnosis

  • Sentiment analysis

  • Fraud detection

Option B: Regression Problem

  • Price prediction (house, stock, product)

  • Demand forecasting

  • Performance estimation

  • Time series prediction

Deliverables:

  • Dataset description (source, size, features)

  • Problem statement

  • Success metrics

  • Exploratory data analysis (EDA)

Grading:

  • Clear problem definition (5 pts)

  • Appropriate dataset (5 pts)

  • EDA with visualizations (5 pts)

Part 2: Model Training with AutoML (25 points)

Use PyCaret, FLAML, or H2O AutoML to train your model.

Requirements:

  • Data preprocessing (handle missing values, encode categoricals)

  • Train multiple models using AutoML

  • Compare at least 5 different algorithms

  • Tune hyperparameters of best model

  • Evaluate performance (accuracy, precision, recall, etc.)

  • Save final model

Deliverables:

  • Jupyter notebook with AutoML pipeline

  • Model comparison results

  • Performance metrics on test set

  • Saved model file (.pkl or .h5)

Grading:

  • Proper data preprocessing (7 pts)

  • Model comparison and selection (8 pts)

  • Performance evaluation (5 pts)

  • Model saved correctly (5 pts)

Part 3: Interactive Interface (30 points)

Build EITHER a Gradio interface OR a Streamlit app.

Option A: Gradio Interface

Requirements:

  • Clear input components for all features

  • Prediction output with confidence score

  • At least 3 example inputs

  • Custom theme and styling

  • Error handling for invalid inputs

Option B: Streamlit App

Requirements:

  • Sidebar for input controls

  • Main area for predictions and visualizations

  • Session state for user interactions

  • Caching for model loading

  • Interactive charts (plotly or matplotlib)

Both Options Must Include:

  • Clear instructions for users

  • Feature descriptions/tooltips

  • Actionable output (not just raw predictions)

  • Professional appearance

Deliverables:

  • app.py file with complete interface

  • requirements.txt with all dependencies

  • Screenshots of working interface

Grading:

  • User-friendly design (10 pts)

  • Proper functionality (10 pts)

  • Error handling and edge cases (5 pts)

  • Professional appearance (5 pts)

Part 4: Deployment (20 points)

Deploy your application to Hugging Face Spaces.

Requirements:

  • Create Hugging Face account

  • Set up new Space (Gradio or Streamlit SDK)

  • Upload all necessary files:

  • Ensure app runs successfully

  • Test all features in deployed version

Deliverables:

  • Public Hugging Face Space URL

  • README.md with:

    • App description

    • Usage instructions

    • Model information

    • Example inputs

  • Working deployed application

Grading:

  • Successful deployment (8 pts)

  • Complete README (5 pts)

  • App functionality (5 pts)

  • Professional presentation (2 pts)

Part 5: Documentation and Report (10 points)

Create a comprehensive report including:

  1. Introduction (1-2 pages)

    • Problem description

    • Dataset overview

    • Approach summary

  2. Methods (2-3 pages)

    • AutoML platform choice and justification

    • Model comparison process

    • Final model selection reasoning

    • Interface design decisions

  3. Results (1-2 pages)

    • Model performance metrics

    • Comparison table

    • Visualizations of results

    • Discussion of strengths/limitations

  4. Deployment (1 page)

    • Deployment process

    • Challenges faced

    • Solutions implemented

  5. Conclusion (1 page)

    • Summary of achievements

    • Lessons learned

    • Future improvements

Deliverables:

  • PDF report (5-10 pages)

  • Well-organized sections

  • Visualizations and tables

  • References (if applicable)

Grading:

  • Clarity and organization (4 pts)

  • Technical accuracy (3 pts)

  • Completeness (3 pts)

🌟 Bonus Points (Up to 15 extra points)

Bonus Options:

  1. Advanced Features (5 pts each, max 10 pts)

    • SHAP explanations for predictions

    • Batch prediction with file upload

    • Model comparison dashboard

    • Real-time data visualization

    • A/B testing different models

  2. Additional Deployment (5 pts)

    • Deploy to multiple platforms (Docker, Cloud Run, etc.)

    • Set up CI/CD pipeline

    • Add monitoring/logging

  3. Outstanding Quality (5 pts)

    • Exceptional documentation

    • Production-ready code quality

    • Comprehensive error handling

    • Accessibility features

📤 Submission Guidelines

Submit the following:

  1. GitHub Repository containing:

    • Jupyter notebook(s) for model training

    • app.py for interface

    • requirements.txt

    • README.md

    • Saved model file

    • Any additional code files

  2. Hugging Face Space URL

    • Working deployed application

    • Complete README on Space

  3. Report (PDF)

    • Following structure outlined above

  4. Submission Form with:

    • Your name and student ID

    • GitHub repository URL

    • Hugging Face Space URL

    • Report PDF

    • Brief summary (200 words)

Submission Format:

  • ZIP file containing all materials

  • Named: LastName_FirstName_Phase17_Assignment.zip

  • Submit via course platform by deadline

✅ Evaluation Rubric

Component

Points

Criteria

Dataset & Problem

15

Clear problem, appropriate data, EDA

AutoML Training

25

Proper workflow, model comparison, evaluation

Interface

30

User-friendly, functional, professional

Deployment

20

Working app, complete README, accessible

Documentation

10

Clear, organized, comprehensive report

Bonus

+15

Advanced features, extra deployment, quality

TOTAL

100 (+15)

💡 Tips for Success

Technical Tips:

  1. Start simple - Get basic version working first

  2. Test locally - Debug before deploying

  3. Version control - Commit frequently

  4. Pin dependencies - Specify exact versions

  5. Handle errors - Add try-except blocks

Design Tips:

  1. User-first - Think about user experience

  2. Clear labels - Make inputs obvious

  3. Provide examples - Help users understand

  4. Show confidence - Display prediction uncertainty

  5. Add context - Explain what predictions mean

Deployment Tips:

  1. Check requirements - Test all dependencies

  2. Optimize model - Reduce size if possible

  3. Test thoroughly - Try edge cases

  4. Monitor logs - Watch build process

  5. Update README - Keep documentation current

📚 Resources

AutoML Platforms:

Interface Tools:

Deployment:

Example Projects:

  • Browse Hugging Face Spaces for inspiration

  • Check course notebooks for patterns

  • Review PyCaret examples

❓ FAQ

Q: Can I use my own dataset?
A: Yes! As long as it meets the requirements (appropriate size, clear problem).

Q: Which AutoML platform should I use?
A: PyCaret is recommended for beginners. FLAML for faster training. H2O for larger datasets.

Q: Gradio or Streamlit?
A: Gradio is simpler for basic interfaces. Streamlit offers more customization.

Q: What if my model is too large to upload?
A: Use model compression, reduce features, or use Git LFS for large files.

Q: How long should my app take to load?
A: Aim for < 30 seconds initial load, < 2 seconds per prediction.

Q: Can I work in a group?
A: Check with your instructor. Usually individual work is required.

Q: What if deployment fails?
A: Check logs, verify requirements.txt, test locally first, ask for help.

🎯 Example Project Ideas

Beginner Level:

  • Iris flower classifier

  • Boston house price predictor

  • Titanic survival predictor

  • Wine quality estimator

Intermediate Level:

  • Customer churn predictor

  • Loan default predictor

  • Product recommendation system

  • Sentiment analyzer

Advanced Level:

  • Real-time fraud detection

  • Medical diagnosis assistant

  • Stock price forecaster

  • Image classification system

🏆 Grading Timeline

  • Week 1: Dataset selection and EDA

  • Week 2: Model training and evaluation

  • Week 3: Interface development

  • Week 4: Deployment and documentation

  • Final: Submit complete project

Late Submission: -10% per day, up to 3 days

📧 Getting Help

  • Office Hours: [Specify times]

  • Discussion Forum: [Link]

  • Email: [Instructor email]

  • Technical Issues: Include error logs and screenshots

Good luck! Build something amazing! 🚀