PyCon India 2025

Pratik Kumar Panda

Hi, my name is Pratik Kumar Panda. I am Currently working at RedHat as an SRE. Primarly working on Openshift Dedicated and ROSA.
In my free time, you can find me building drones and hobby robots - tinkering with Raspberry Pis, Arduinos, and automation with Python


Professional Link

https://www.linkedin.com/in/devppratik/

Preferred Pronoun

He/Him

Speaker Tagline

SRE | K8s, Cloud & Open Source

Gravatar - Professional Photo

https://gravatar.com/scrumptiousalways782955c1b2

LinkedIn Profile

https://www.linkedin.com/in/devppratik/

Instagram Profile

https://www.instagram.com/devppratik/


Sessions

09-12
14:00
180min
Offline-First Python IoT: Mesh, Security & OTA on the Edge
Pratik Kumar Panda

What happens when your IoT devices lose connectivity — for hours, days, or forever? In this hands-on workshop, participants will build and emulate a fleet of Python-powered edge devices that store data locally, communicate over a peer-to-peer mesh, and receive secure over-the-air updates — all without relying on the cloud.
Using only Python and your laptop, you'll simulate a complete offline-first IoT stack: from runtime logic and local storage to mesh networking and cryptographically signed OTA delivery. Ideal for developers working on rural tech, disaster recovery, embedded platforms, or privacy-preserving systems.

Python for Hardware, Edge Computing, IoT
Room 4
09-14
15:30
30min
Edge ML with MicroPython + TinyML: Real-time Inference on under ₹500 Boards
Pratik Kumar Panda, Sneha Singh

Machine learning is moving closer to the edge—but how close can you really get with Python and a ₹500 board? This session dives into running real-time ML inference on ultra-low-cost microcontrollers like the ESP32 using MicroPython and TinyML.

You’ll learn how to deploy models that classify audio, detect gestures, or monitor anomalies—all without an internet connection, operating system, or expensive tooling. This talk demystifies edge ML by showing how Python can still play a meaningful role in deeply constrained environments. If you're working in embedded systems, edge computing, or low-power AI, this session shows how to unlock a new layer of intelligence at the edge—one tiny inference at a time.

AI, ML, Data Science
Track 1