Tensorflowlite

TensorFlow Lite is an open-source deep learning framework designed for on-device inference, commonly referred to as Edge Computing. It enables developers to deploy trained machine learning models directly on edge devices such as mobile phones, embedded systems and IoT devices without relying on cloud resources.

• It is a production-ready, cross-platform framework used for deploying ML on mobile devices and embedded systems.
• Designed specifically for on-device and edge machine learning
• Supports platforms like Android, iOS, embedded Linux and microcontrollers (MCUs)
• Enables execution of trained models directly on edge devices
• Reduces dependency on cloud-based inference
• Ideal for real-time applications such as object detection and image recognition

• Train the Model: Build and train the machine learning model using TensorFlow on high-performance systems.

• Convert to TensorFlow Lite: Convert the trained model into .tflite format using the TensorFlow Lite Converter, applying optimizations if required.

• Optimize the Model: Apply techniques like quantization to reduce model size and improve inference speed.

• Deploy on Edge Device: Integrate the .tflite model into mobile, embedded or IoT applications.

• Run Inference: Execute the model using the TensorFlow Lite interpreter for fast, on-device predictions.

  TensorFlow Lite uses FlatBuffers as its model file format instead of Protocol Buffers. FlatBuffers allow direct access to serialized data without an unpacking step resulting in faster execution and lower memory usage which is crucial for edge and mobile environments.

In simpler terms:

TensorFlow Lite was the deployment/runtime layer for running TensorFlow-style machine learning models efficiently on constrained devices.

Traditional TensorFlow was powerful but heavy. It was built for:

• Server-side training