Deploying Machine Learning Models with FastAPI and Angular
The code that accompanies this article can be found here. In this article, we explore how we can prepare a machine learning model for production and deploy it inside of simple Web application. Deployment of Machine Learning models is an art for itself. In fact, to...
PyTorch for Beginners – Deploying Models with TorchServe
In the previous articles, we covered some PyTorch basics. First, we explored tensors, gradients and how we can use these concepts to write machine learning algorithms using this framework. Then we utilized that knowledge and used Pytorch for its main purpose –...
Deploying Machine Learning Models – pt. 3: gRPC and TensorFlow Serving
The code presented in this article can be found here. In the previous articles, we explored how we can serve TensorFlow Models with Flask and how we can accomplish the same thing with Docker in combination with TensorFlow Serving. Both of these approaches utilized...
Deploying Machine Learning Models – pt. 2: Docker & TensorFlow Serving
In the previous article, we started exploring the ways one deep learning model can be deployed. There we decided to run a simple Flask Web app and expose simple REST API that utilizes a deep learning model in that first experiment. However, this approach is not very...
Deploying Machine Learning Models – pt. 1: Flask and REST API
In this article, which is the first in the series, we explore how we can prepare a deep learning model for production and deploy it inside of Python Web application. This is just the first step in the long journey. In fact, deployment of Deep Learning models is an art...
