🚀Serving Multiple Models In BentoML | Step-by-Step Tutorial

In real-world ML systems, you rarely deploy just one model. You often need multiple models working together, sometimes sequentially, sometimes concurrently, all inside a single production service. In this tutorial, you’ll learn how to use BentoML to compose multiple ML models into one unified service. We’ll cover: 🧩 Serving multiple models as a single API service 🔁 Running models in sequential pipelines (Model A → Model B) ⚡ Executing models in concurrent/parallel mode 🏗️ Structuring a clean, production-ready Bento service 🚀 Best practices for scalable ML system design 🧠 What You’ll Learn ✅ How model composition works in BentoML ✅ Packaging multiple trained models ✅ Designing a sequential inference pipeline ✅ Implementing parallel model execution ✅ When to use sequential vs concurrent architectures 🏗️ Why This Matters In production ML systems: You may use one model for preprocessing and another for prediction. You may combine a classifier + ranking model. You may run multiple models in parallel and aggregate outputs. Understanding model composition is key for: ML Engineers MLOps practitioners AI backend developers Anyone preparing for ML system design interviews Join this channel to get access to perks: https://www.youtube.com/channel/UCFKxdpoc4KdMjUaAsMi7gmg/join