Common Challenges and Best Practices in Machine Learning

⚠️ Challenges & Best Practices in Machine Learning Projects

🧱 Common Challenges

Machine learning projects often encounter:

• 🚫 Data scarcity
• ⚖️ Bias
• 🧠 Overfitting
• 🔍 Interpretability
• 💻 Computational costs

🛠️ Addressing Challenges

• 🌍 Ensure diverse and balanced datasets to mitigate bias
• 🛡️ Use regularization and cross-validation to prevent overfitting
• 🧭 Leverage model interpretability tools like:
  • 📊 SHAP
  • 🔎 LIME
• ⚙️ Optimize model efficiency using:
  • ✂️ Pruning
  • ⚖️ Quantization

✅ Best Practices

• 🎯 Clear problem definition
• 🔁 Iterative experimentation
• 🧪 Rigorous validation
• 📝 Comprehensive documentation
• 👥 Build a multidisciplinary team to address blind spots

🌱 Analogy: ML Development as Gardening

Developing ML systems is like tending a garden:

• 🌦️ Understand the environment (data)
• 🌱 Provide consistent care
• 🔄 Adapt to changing conditions

👉 These lead to a healthy, productive system.