Deploying Ai On Edge Devices Development Tutorials, Guides & Insights

import tensorflow as tf
import numpy as np

# Load the TensorFlow Lite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Prepare the input data
input_data = np.array([[temperature]], dtype=np.float32)

# Make predictions
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()

# Get classification results
output_data = interpreter.get_tensor(output_details[0]['index'])
print(f'Predicted Class: {output_data}')

Edge computing offers significant advantages for IoT systems, especially when combined with Python for data processing and AI model integration. By following this tutorial, you’ve learned how to: