Can we use streaming data to train and use a model continuously and improve it at the same time?
The ability to use streaming data for both continuous model training and real-time inference is a significant topic in machine learning, particularly within modern data-driven applications. The traditional approach to building machine learning models typically involves collecting a batch of data, cleaning and preparing it, training a model, evaluating it, deploying it, and then periodically
What role did TensorFlow Lite play in the deployment of the models on the device?
TensorFlow Lite plays a important role in the deployment of machine learning models on devices for real-time inference. It is a lightweight and efficient framework specifically designed for running TensorFlow models on mobile and embedded devices. By leveraging TensorFlow Lite, the Air Cognizer application can effectively predict air quality using machine learning algorithms directly on