In this course you will learn how to deploy Machine Learning Models using various techniques.

Course Structure:

1. Creating a Model

2. Saving a Model

3. Exporting the Model to another environment

4. Creating a REST API and using it locally

5. Creating a Machine Learning REST API on a Cloud virtual server

6. Creating a Serverless Machine Learning REST API using Cloud Functions

7. Deploying TensorFlow and Keras models using TensorFlow Serving

8. Deploying PyTorch Models

9. Creating REST API for Pytorch Models

10. Tracking Model training experiments and deployment with MLfLow

Python basics and Machine Learning model building with Scikit-learn will be covered in this course. TensorFlow and Pytorch model building is not covered so you should have prior knowledge in that. Focus of the course is mainly Model deployment.