Deploy Your AI Agent Project to Production with FastAPI and a Vector DB
In this hands-on workshop, you’ll learn how to take an AI agent or RAG-style project and deploy it as a production-ready application using FastAPI and a vector database.
We’ll focus on the practical engineering work needed to move beyond experimentation: structuring the backend, creating API endpoints, integrating retrieval, handling configuration, and preparing the system for real-world use.
You’ll build a working backend that can ingest documents, retrieve relevant context, and serve grounded responses through an API.
More importantly, you’ll see how to organize the project in a way that makes it easier to deploy, maintain, and extend.
What you’ll learn
How to turn an AI prototype into a deployable backend service
How to structure a FastAPI app for an AI agent or RAG workflow
How to connect a vector database for retrieval in production-style setups
How to expose clean endpoints for querying and indexing
What to consider when preparing your app for real-world deployment
What you’ll build
A FastAPI backend for your AI project
A document ingestion and indexing pipeline
A retrieval layer powered by a vector DB
An API endpoint for grounded responses
A foundation you can deploy and extend further
This session is for people who already have built or explored AI projects and now want to understand what it takes to ship them properly.
Important: This event is invite-only and available exclusively to AI Shipping Labs members. To join, you need to be part of the community.
Please register using the same email address you used to sign up for AI Shipping Labs, so we can verify your membership.
About the speaker
Alexey Grigorev is a software engineer and machine learning practitioner with 15+ years of experience building production ML systems. He focuses on practical, production-grade ML and AI systems, from early prototypes to reliable production systems.
At AI Shipping Labs, Alexey is building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, he wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects.