Data Engineering for Multimodal AI

Chapter 8. Applying MLOps for Multimodal AI Applications

When you think of traditional ML, you’re probably envisioning models that work primarily with single data types or structured data. Multimodal AI has ML at its core; ML provides the algorithms and techniques that allow AI systems to learn from and process different types of data. However, multimodal AI also expands on this by leveraging more advanced ML architectures, like deep learning, to handle the complexity of integrating diverse data sources.

In this chapter, we’ll explore the operational frameworks, tools, and practices essential for managing multimodal AI systems in production. With the additional complexity of multimodal data pipelines (conveying text, image, audio, video, and sensor data), MLOps must address data orchestration, experiment tracking, CI/CD workflows, automated retraining, and collaboration across teams. To this end, MLOps combines machine learning (ML) development with operations (Ops) to ensure that AI systems are scalable, reliable, and continuously optimized.

This chapter will provide a comprehensive roadmap for implementing modern MLOps strategies, enriched with cutting-edge tools, emerging practices, and practical insights. It will conclude with a step-by-step playbook for building a robust MLOps pipeline for multimodal AI.

Throughout this chapter, we will anchor ourselves in the journey of ForgeSense, a fictional multimodal AI company that builds large behavior models (LBMs). LBMs power robotics ...