Discussing the deployment process of Machine Learning models. Deployment is an important part of any Machine Learning cycle, the main application of Deployment is letting the end-users use the ML models and in order to collect real-time data, that is prone to noise.

Also discussing various methods by which a DL/ML model can be deployed in production. I will also be discussing related data pipelines, specifically for Image and numerical data, that are important for IoT applications like remote sensing and drones. These methods will be including

Azure VM service

Azure App/Web Service

Heroku

Azure Functions - Serverless

API Generation

Docker - Containerization

Edge Devices - NVIDIA JETSON NANO, Raspberry PI, etc