In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.  This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples

Course Structure:

1. Creating a Classification Model using Scikit-learn

2. Saving the Model and the standard Scaler

3. Exporting the Model to another environment - Local and Google Colab

4. Creating a REST API using Python Flask 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. Building and Deploying TensorFlow and Keras models using TensorFlow Serving

8. Building and Deploying  PyTorch Models

9. Converting a PyTorch model to TensorFlow format using ONNX

10. Creating REST API for Pytorch and TensorFlow Models

11. Deploying tf-idf and text classifier models for Twitter sentiment analysis

12. Deploying models using TensorFlow.js and JavaScript

13. Tracking Model training experiments and deployment with MLFLow

14. Running MLFlow on Colab and Databricks

Python basics and Machine Learning model building with Scikit-learn will be covered in this course.  This course is designed for beginners with no prior experience in Machine Learning and Deep Learning

You will also learn how to build and deploy a Neural Network using TensorFlow Keras and PyTorch. Google Cloud (GCP) free trial account is required to try out some of the labs designed for cloud environment.