Unify Multimodal Data with Automated ETL

Overview

Did you know that multimodal AI systems often fail not because of weak models, but because their underlying data pipelines cannot reliably unify text, image, audio, and tabular features? A strong multimodal infrastructure is the foundation of advanced AI. This Short Course was created to help professionals in this field build robust data infrastructure for multimodal AI applications and automate the processing of diverse data types including text, images, and audio. By completing this course, you will be able to design unified schemas for multimodal feature storage and implement automated ETL pipelines using workflow orchestration tools, giving you the ability to support scalable, production-ready multimodal AI systems. By the end of this 4-hour long course, you will be able to: Create a unified data schema for storing multimodal machine learning features. Implement automated ETL pipelines using a workflow orchestration tool. This course is unique because it combines multimodal feature engineering with automation and orchestration, equipping you to transform fragmented datasets into cohesive, high-quality pipelines that power next-generation AI models. To be successful in this project, you should have: Database design fundamentals Basic ETL concepts SQL proficiency Familiarity with cloud storage ML feature engineering basics

Syllabus

Module 1: Create Unified Data Schema for Multimodal ML Features

Learners will design and implement unified data schemas that efficiently store and organize multimodal machine learning features across text, image, and audio data types.

Module 2: Implement Automated ETL Pipelines with Workflow Orchestration

Learners will build and deploy automated ETL pipelines using Apache Airflow to process multimodal data from raw sources into machine learning-ready features with proper error handling and monitoring.