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Machine Learning Model Deployment on Edge Devices - Part 1

#edgeanalytics #machinelearning #quantization In this video we will be talking on how model can be optimized to run on low compute and low memory devices. We will be using TFlite to reduce model size and further quantize it to prune weights. I will also be benchmarking performance between regular Keras model and tflite model Check out my edge analytics and model quantization videos if you want to understand more on it Edge Analytics -    • Edge Analytics (Machine Learning mode...   Post Model Quantization -    • Model Quantization in Deep Neural Net...   Also below is my entire collection of model deployment videos Part 1 - Overview -    • Model Deployment Overview - Part 1   Part 2 - Deployment Patterns -    • Machine Learning Model Deployment Pat...   Part 3 - Deployment explained with use case -    • Model Deployment Architecture explain...   Part 4 - Methods and techniques -    • Model Deployment - Containers and Kub...   Part 5 - Why Ngnix and Gunicorn -    • Model Deployment Methods and Techniqu...   Part 6 - Streaming Deployment -    • Model Deployment - Why Ngnix Gunicorn...   Hands On Part 1 - Understanding Model pipeline -    • Model Deployment - Tensorflow Model p...   Part 2 - Model deployment using Flask -    • Machine Learning Model Deployment usi...