AI and Machine Learning in Production

# AI and Machine Learning in Production

Taking machine learning models from jupyter notebooks to production is a journey filled with challenges. Here's what you need to know.

## The Production Gap

Training a model is just the beginning. Production ML systems require:

- **Model versioning and reproducibility**

- **Real-time inference pipelines**

- **Continuous monitoring and retraining**

- **A/B testing infrastructure**

## Key Architecture Components

**Feature Store**

Centralized storage and serving of ML features ensures consistency between training and inference.

**Model Registry**

Track model versions, metadata, and performance metrics in a central repository.

**Monitoring**

Watch for data drift, model degradation, and performance issues in real-time.

Success in production ML is 10% models and 90% engineering.