Hosting Models with TF Serving on Docker

Training a Machine Learning (ML) model is only one step in the ML lifecycle. There's no purpose to ML if you cannot get a response from your model. You must be able to host your trained model for inference. There's a variety of hosting/deployment options that can be used for ML, with one of the most popular being TensorFlow Serving. TensorFlow Serving helps take your trained model's artifacts and host it for inference.