AI Infrastructure: Overview
Understand the critical infrastructure behind AI projects, including data ingestion, cleaning, model training, and deployment. Learn how cloud-based ML services simplify AI development and support creating scalable, efficient AI solutions.
We'll cover the following...
Introduction
As we learned in the introductory lessons, creating smart applications means moving away from the traditional programming paradigm of hard-coded logic to generic algorithms that are hungry for data and computational power. This in turn means that we cannot use traditional architectures when designing AI-driven solutions; we need to introduce some new infrastructure blocks and tools.
First-timers are often surprised by how little time in a machine learning or AI project is spent actually doing model training or learning algorithms. As the ...