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
