Unstructured Data Processing with a Raspberry Pi AI Kit and Python
summary
A portable pipeline captures webcam frames on a Raspberry Pi 5 with a $70 AI hat, runs object detection and pose estimation (ResNet34/pose nodes), vectorizes images and pose embeddings, and streams them into Milvus vector database for semantic search and RAG. It sustains roughly 10 fps on capture, uploads images with metadata, and performs multimodal text+image queries and a local re‑rank using lava 7B / LLaMA models that can take ~1 minute — the build works end to end.
AI-written summary · about
chapters (14)
- 0:00Introduction
- 3:17Unstructured data importance
- 6:51Raspberry Pi AI Kit hardware
- 9:00Pose estimation
- 12:13Real-time capture & Slack
- 14:21Vector database setup
- 17:38Multi-modal search
- 20:12Advanced queries & ranking
- 24:05Performance limits
- 26:57Remote access with Pi Connect
- 29:48Frame rates & applications
- 31:16Pi vs Jetson
- 32:20Key takeaways
- 35:32Q&A
related talks
36m"Lights, camera, AI!" - Automated sports videography
44mAdventures in Puppy Training with A.I. and a Raspberry Pi
21mAI, Embedded System and Robotics
29mSeeing with Python
53mFROM ZERO TO DEEP LEARNING WITH SCALA
28mChasing birds with code
17mEfficient & Frugal Image Data Processing Pipelines
20mComputer Vision at the Edge with OpenVINO