# AWS SageMaker Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/aws-sagemaker
> Markdown URL: https://aitinkerers.org/technologies/aws-sagemaker.md
> Technology record last updated: 2026-02-25T14:43:34Z
> Generated: 2026-08-24T06:48:02Z

AWS SageMaker is the fully managed service for building, training, and deploying machine learning (ML) models at scale, moving your projects from concept to production fast.

SageMaker delivers a complete, end-to-end ML platform: a single environment for the entire workflow. Use SageMaker Studio for a unified IDE experience, or leverage SageMaker Autopilot to automatically build high-quality models with zero code. It handles the heavy lifting—compute provisioning, distributed training, and model hosting—so your team focuses on data science, not infrastructure. This accelerates model deployment, often achieving production readiness in days, not months, while providing robust MLOps tools for governance and monitoring.

- Official technology site: https://aws.amazon.com/sagemaker/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AutoML Agent: Automating ML Code Generation](https://toronto.aitinkerers.org/talks/rsvp_D5m-tfkILPA)

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.

- Event context: AI Tinkerers Toronto - November 2024 Meetup at Accenture — 2024-11-28 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_D5m-tfkILPA

## Related Technologies

- [AWS Lambda](https://aitinkerers.org/technologies/aws-lambda) ([Markdown](https://aitinkerers.org/technologies/aws-lambda.md)) — 11 public demos
- [LangChain](https://aitinkerers.org/technologies/langchain) ([Markdown](https://aitinkerers.org/technologies/langchain.md)) — 444 public demos
- [MLflow](https://aitinkerers.org/technologies/mlflow) ([Markdown](https://aitinkerers.org/technologies/mlflow.md)) — 5 public demos
- [Phi-3](https://aitinkerers.org/technologies/phi-3) ([Markdown](https://aitinkerers.org/technologies/phi-3.md)) — 2 public demos