GCP AI Fundamentals - AIML Series 6 - Production ML Systems
Introduction In today's rapidly evolving AI landscape, production machine learning (ML) systems have become crucial for businesses seeking to leverage data-driven insights and automate complex decision-making processes. Google Cloud Platform (GCP) offers a robust suite of tools and services to build, deploy, and manage ML models at scale. This article, part of the AIML Series, delves into the fundamentals of architecting production ML systems on GCP, covering essential aspects from data extraction to deploying hybrid models. Whether you're an AI novice or an experienced data scientist, this guide aims to equip you with the knowledge and best practices to excel in your ML endeavors. Section 1: Architecting Production ML Systems Data Extraction, Analysis & Prep Data is the backbone of any ML system. Efficiently extracting, analyzing, and preparing data is critical to building robust models. Here’s how you can leverage GCP tools for these tasks: 1. Data Extraction: Cloud Da