AutoML Agent: Automating ML Code Generation

This demo will showcase a custom proof of concept AI Agent that autonomously generates and executes Python code to find the best machine learning model for a given problem. Built from scratch this agent addresses limitations common in agentic frameworks like Langchain or CrewAI, providing enhanced flexibility and control for production-grade applications.

1. Live Demo

We’ll begin with a live Colab notebook demo, which attendees can access to try the application during the presentation or later. Built entirely in Python with open-source libraries and LLMs, this demo enables hands-on participation.

2. Key Components

We’ll then walk through the application’s architecture, covering:
State Management: Using the transitions library, we manage the state machine workflow to handle tasks such as entity extraction, code generation, error handling, and re-execution, ensuring modular and resilient task transitions.
Open-Source LLM: We use an open source Phi 3.5 model for core tasks like code generation, entity extraction, and error management, demonstrating the capabilities of open-source LLMs for AutoML.

3. Production Architecture Overview

Finally, a production-ready architecture, visualized on Miro, demonstrates an end-to-end production solution on AWS, including:
Frontend and API Layer: Allowing user interaction through a secure, scalable API and frontend interface.
Load Balancing and Model Deployment: AWS Lambda with load balancing optimize for scalable, responsive model deployment.
Security: VPCs, and user verification via AWS Cognito ensure controlled access and data security.
Logging and Monitoring: Model monitoring and tracing via MLFLow on AWS Sagemaker.