Advanced MLOps
Predictive
Architecture
Building professional, audit-ready machine learning systems for student performance prediction.
Module 1: Environment & Setup
Prepare your professional MLOps environment with a clean, modular structure.
Virtual Env
Dependency Sync
# Create environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install Core Stack
pip install pandas scikit-learn matplotlib seaborn xgboost imbalanced-learnModule 2: Data Workflow (Fase 1 & 2)
Data Loading & EDA
Proteksi FileNotFoundError, auto-detect separator, dan analisa distribusi kelas untuk mendeteksi imbalance.
import pandas as pd
def load_data(path):
try:
df = pd.read_csv(path)
print(f"Dataset Loaded: {df.shape}")
return df
except FileNotFoundError:
print("Error: File not found.")Preprocessing
Cleaning duplikat, imputasi median/modus, label encoding, dan stratified split untuk menjaga distribusi target.
Module 3: Advanced Feature Engineering
Feature Penambahan
Reduksi & Scaling
Architect's Note
"Feature selection is where the magic happens. Use PCA only when you have high dimensionality that hurts performance."
Module 4: Model Comparison & Tuning
Fase audit 10 model kandidat (Random Forest, XGBoost, SVM, dsb) dengan SMOTE untuk menangani class imbalance.
Hyperparameter Tuning
Menggunakan GridSearchCV atau RandomizedSearchCV untuk menemukan parameter optimal bagi model terbaik.
python main.py --tune-hyperparameters --save-modelsModule 5: Evaluation & Reporting
Precision/Recall
Penting untuk kasus imbalance seperti prediksi kelulusan.
Confusion Matrix
Visualisasi langsung performa model di setiap kelas.
Cross-Val
Memastikan model tidak overfitting pada satu subset data.
Final Automation
Jalankan seluruh pipeline dengan satu perintah:
python main.pyVerification
Final Graduation
1. Which phase is responsible for SMOTE and hyperparameter tuning?
2. Where are the trained models and preprocessors saved?
Select all correct answers to graduate