Stop watching. Start building.
Build Production
Edge AI Pipeline.
Most courses stops at a notebook. This one delivers on hardware — Sensor, Model, Deployment, done. Embedded, IoT, EdgeAI & GenAI, taught through real builds, not toy demos.
# Edge AI + MQTT: Deploy on Raspberry Pi / Jetson import paho.mqtt.client as mqtt from tflite_runtime.interpreter import Interpreter import numpy as np # Load Edge AI model (TensorFlow Lite) interpreter = Interpreter(model_path="edge_model.tflite") interpreter.allocate_tensors() # MQTT: Connect to broker for IoT telemetry client = mqtt.Client() client.connect("broker.hivemq.com", 1883) pred = run_inference(interpreter, sensor_data) client.publish("edge/predictions", pred) client.subscribe("sensor/data") client.loop_forever()|
✓ TFLite model loaded on edge device
✓ MQTT broker connected (broker.hivemq.com:1883)
→ Listening on topic: "sensor/data"..._
Build with
+3You know the theory. You've never shipped it.
You've followed along with someone else's Colab notebook. You can explain what a quantized model is — but you've never taken a project from a sensor's raw voltage reading to a live prediction running on hardware you can hold.
Where most courses end
Notebook Theory
- ✕Colab notebooks
- ✕Toy, pre-cleaned datasets
- ✕Model never leaves the browser
Where this course ends
Production Hardware
- ✓Real sensors, real noise
- ✓Deployed on physical hardware
- ✓Shipped the way products ship
That gap is what keeps you stuck at "I know the theory" instead of "I've shipped this."
Build with the modern Embedded, IoT & AI stack
Espressif
STM32
ARMRaspberry PiPython
PyTorchTensorFlow
OpenCVDocker
NVIDIAThe Course
Build a Production Edge AI Pipeline
EdgeAI from fundamentals to advanced, in one build. Start with raw sensor data on an ESP32 or STM32 — leave having trained, quantized, and deployed a model to a Raspberry Pi or Jetson, with live predictions running on a real dashboard.
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Join the WaitlistWho this is for
Whoever you are — student, working engineer, or just curious about how hardware and AI actually connect — there's a place to start.
You're building your skills
Whether you're a student, a software engineer moving into hardware, or an AI enthusiast who's never touched a soldering iron — you don't need ten half-finished projects on your GitHub. You need one you can walk a hiring manager through, line by line.
View the CoursesYou're training a team
Live, hands-on embedded and edge AI training — built around your team's actual hardware and stack, not a fixed slide deck.
Explore Corporate TrainingThree steps to a deployed system
Pick up the hardware
ESP32, STM32, Raspberry Pi, or Jetson — you're working on real devices from lesson one.
Build the full pipeline
Sensor → firmware → model → deployment → dashboard. Every step is one you'll use again.
Ship something real
Walk away with a deployed system and a public repo — not just a certificate.
Embedded and edge AI training, taught by someone who ships it.
Rajath Kumar K S has delivered hands-on embedded and IoT training directly to engineering teams at ISRO and Broadridge — sessions built around real hardware and real deployment.
Explore Corporate Training →Stop watching. Start shipping.
Real systems. Pipelines you can defend in an interview. Pick a course, project and build something that actually you can hold.