AI/ML CheatSheet : Must-Know Tips & Tricks for AI Engineers

12. DEPLOYMENT AND PRODUCTION

12.1) Model Serialization

What it means:

• After training your machine learning model, you save (serialize) it to use later without retraining.

Popular tools:

• Pickle – A Python library to serialize and deserialize Python objects.
• Joblib – Similar to Pickle but better for large NumPy arrays.

12.2) Flask/Django for Model Deployment

These are web frameworks that let you expose your model as an API endpoint, so other apps or users can access it via the internet.

• Flask: Lightweight and easier for quick ML model APIs.
• Django: Heavier but better for large web applications with built-in admin, security, and ORM.

12.3) Serving Models with TensorFlow Serving, FastAPI

TensorFlow Serving: Used to deploy TensorFlow models in production. It supports versioning and high-performance serving with REST/gRPC.
FastAPI: A modern, fast (high-performance) framework for building APIs with automatic docs, great for production-grade ML APIs

12.4) Monitoring and Maintaining Models in Production

Once your model is live, you need to ensure it continues to perform well.

What to monitor:

• Model accuracy degradation (due to data drift)
• Response time
• Error rates
• System metrics (CPU, memory)

Tools:

• Prometheus + Grafana for system and application monitoring
• MLflow or Evidently.ai for tracking model performance over time

13. PRACTICE & COMMON BEGINNER MISTAKES

13.1) Common Beginner Mistakes

General ML Mistakes

• Using test data during training
• Not normalizing/scaling features
• Ignoring class imbalance in classification tasks
• Forgetting to check for data leakage
• Not splitting the dataset correctly (Train/Validation/Test)

Neural Network Mistakes

• Using too many/too few layers without tuning
• Choosing wrong activation/loss functions
• Ignoring overfitting (no dropout or regularization)

NLP Mistakes

• Feeding raw text without preprocessing
• Using TF-IDF on small datasets without context
• Confusing stemming with lemmatization

Deployment Mistakes

• Not checking model performance after deployment
• Ignoring real-time latency
• No monitoring/logging in place