Serverless LLMs and Agentic AI with Modal – Lesson 5

💖 Support BrainOmega ☕ Buy Me a Coffee: https://buymeacoffee.com/brainomega 💳 Stripe: https://buy.stripe.com/aFa00i6XF7jSbfS9T218c00 💰 PayPal: https://paypal.me/farhadrh 🎥 In this video, we continue our Serverless LLMs and Agentic AI course with Lesson 5: Volumes – Persistent Storage Across Containers in Modal. After learning how to control environments, scaling, and hardware resources, we now tackle a critical missing piece of serverless systems: state. Most serverless functions are stateless by default, but real AI systems need to persist results, metrics, checkpoints, and artifacts across runs. In this lesson, you’ll learn how Modal Volumes solve this problem cleanly and safely. This lesson is fully hands-on and systems-oriented. We build a small Experiment Tracker that logs benchmark results into a shared Modal Volume using a simple JSONL format. You’ll see how multiple functions can read and write the same persistent data across different runs, days, or even weeks. Along the way, you’ll learn the two most important operational concepts when working with volumes: commit(), which makes writes durable and visible to other containers, and reload(), which ensures long-lived or reused containers see the latest data instead of stale state. By the end of this lesson, you’ll understand how to design stateful serverless AI workflows without databases, how to avoid subtle consistency bugs, and how to use volumes for experiment tracking, caching, checkpoints, and intermediate artifacts. This mental model is essential for building real-world LLM systems—whether you’re logging embedding benchmarks, storing RAG indexes, or persisting agent memory across runs. 💻Code on GitHub: https://github.com/frezazadeh/serverless-llm-agentic-ai/blob/main/lesson5.ipynb ⸻ 📚 What You’ll Learn • What Modal Volumes are and how they provide persistent storage • How to create and mount named volumes in Modal • How to write and read shared files across containers and runs • Why co