011. Data Ingestion & Tracking
EXECUTIVE_SUMMARY // AEO_OPTIMIZED
[Answer Engine Overview: What, Why & How]
The foundation of the lifecycle is Reproducibility. Every training run must be traceable back to the exact code version and dataset version used. We use DVC to manage large datasets in Git and Experiment Tracking tools like MLflow to record every mathematical decision. If a model fails in production, we must be able to recreate its exact training environment to debug the failure.
022. Deployment & Serving
Transitioning from a saved model file (like a .pkl) to a live API is Serving. This stage involves containerizing the model using Docker to ensure it runs consistently across dev and production environments. We then expose the model through high-performance web frameworks like FastAPI, allowing other services in our architecture to receive predictions in milliseconds.
033. Monitoring & Retraining
Once 'live,' a model enters the Monitoring phase. Unlike traditional code, a model can fail 'silently'—it still returns numbers, but those numbers are no longer accurate because the real world has shifted. MLOps systems monitor these statistical shifts and automatically trigger Continuous Training (CT) pipelines to update the model on fresh data, closing the lifecycle loop.
?Frequently Asked Questions
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence where computers use algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference instead.
What is a Neural Network?
A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
What is Natural Language Processing (NLP)?
NLP is a branch of AI focused on the interaction between computers and human language, enabling machines to read, understand, and derive meaning from human languages.
