Machine Learning Project Workflow | Allah-u-Abha Rodrigues

Overview

This documentation provides a complete methodology for implementing machine learning projects from conception to production deployment. Based on practical experience from the SalaryPredictionML project and industry best practices.

The workflow emphasizes reproducibility, scalability, and maintainability - essential factors for successful ML projects in production environments.

Complete Workflow

Data Collection & Preprocessing

Data Acquisition

• Identify reliable data sources (APIs, databases, files)
• Implement data collection pipelines
• Establish data quality validation rules
• Document data schema and sources

Preprocessing Pipeline

• Handle missing values (imputation strategies)
• Normalize/standardize numerical features
• Encode categorical variables
• Feature engineering and selection

Code Example: Data Preprocessing

import pandas as pd
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.impute import SimpleImputer

def preprocess_data(df):
    # Handle missing values
    numeric_imputer = SimpleImputer(strategy='median')
    categorical_imputer = SimpleImputer(strategy='most_frequent')
    
    # Feature engineering
    df['experience_log'] = np.log1p(df['years_experience'])
    df['salary_per_experience'] = df['salary'] / (df['years_experience'] + 1)
    
    return df

Model Development

Algorithm Selection

• Baseline models (Linear Regression, Decision Trees)
• Advanced algorithms (Random Forest, XGBoost)
• Ensemble methods for improved performance
• Hyperparameter optimization strategies

Training Process

• Cross-validation for robust evaluation
• Feature importance analysis
• Learning curves and validation plots
• Model checkpointing and versioning

Model Training Pipeline

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV

# Hyperparameter tuning
param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [10, 20, 30, None],
    'min_samples_split': [2, 5, 10]
}

rf_grid = GridSearchCV(
    RandomForestRegressor(random_state=42),
    param_grid, cv=5, scoring='r2'
)

rf_grid.fit(X_train, y_train)

Evaluation & Validation

Metrics

• R² Score
• RMSE
• MAE
• MAPE

Validation

• Train/Valid/Test Split
• K-Fold Cross Validation
• Time Series Split
• Stratified Sampling

Analysis

• Feature Importance
• Residual Analysis
• Bias-Variance Trade-off
• Error Distribution

Deployment Strategies

Local Deployment

• Streamlit for rapid prototyping
• Flask/FastAPI for REST APIs
• Docker containerization
• Environment management

Cloud Deployment

• AWS SageMaker for ML workflows
• Heroku for simple web apps
• AWS Lambda for serverless
• Model serving with MLflow

Streamlit Deployment Example

import streamlit as st
import joblib
import pandas as pd

# Load trained model
@st.cache_resource
def load_model():
    return joblib.load('salary_prediction_model.pkl')

model = load_model()

# User input interface
st.title("Salary Prediction Tool")
experience = st.slider("Years of Experience", 0, 30, 5)
education = st.selectbox("Education Level", options)

# Make prediction
if st.button("Predict Salary"):
    prediction = model.predict([[experience, education, ...]])
    st.success(f"Predicted Salary: {prediction[0]:,.2f{'}'}")

Production Monitoring

Performance Monitoring

• Model accuracy tracking over time
• Prediction latency measurements
• Error rate monitoring
• Resource usage analytics

Data Drift Detection

• Input feature distribution changes
• Statistical drift tests
• Automated retraining triggers
• Model versioning and rollback

Best Practices

Do's

• Version control your data and models
• Document assumptions and decisions
• Implement comprehensive testing
• Use reproducible random seeds
• Monitor model performance continuously
• Implement gradual rollouts

Common Pitfalls

• Data leakage in feature engineering
• Inadequate validation strategies
• Ignoring class imbalance
• Over-optimization on validation set
• Poor error handling in production
• Lack of model interpretability

Recommended Tools

Python

Pandas

Scikit-learn

NumPy

Jupyter

MLflow

Docker

Streamlit

AWS SageMaker

Git/GitHub

pytest

Matplotlib

This tech stack provides a comprehensive foundation for ML projects, from development to deployment and monitoring. Choose tools based on your specific requirements and team expertise.

Real Project Example

SalaryPredictionML Project

This workflow was successfully implemented in the SalaryPredictionML project, achieving 85% accuracy in predicting software developer salaries using Stack Overflow survey data.

Read Case Study View Code