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Udemy Deploy Machine Learning Models on GCP + AWS Lambda (Docker)

Video Introducing this tutorial


1. Introduction:
1. Deployment Overview
2. reviews
3. Course - FAQ
4. Join Online Classroom
5. Machine Learning Workflow
6. Different Model Deployment Option

2. Code Download:
1. Code Download

3. Flask Basics:
1. Introduction to Flask & Setup environment
2. Download and Install Anaconda
3. Create Virtual environment
4. Install Library
5. Spyder IDE
6. Flask Introduction
7. (Hands-on) Flask Hello World
8. (Hands-on) Flask Web app - With parameter
9. Quiz

4. Deploying machine learning (Scikit Learn) model to Flask:
1. Section Introduction
2. Data Preparation & Create Model
3. (Hands-on) Serialize & Deserialize Scikit-learn Model
4. (Hands-on) Deploying model to Flask Web application
5. Test Webservice through Postman +Python requests

5. Model Serialization with Tensorflow 2.0:
1. Build Neural Network Model - keras (Tensorflow 2.0)
2. (Hands-on) Serialize and Deserialize model

6. Deploy model on Heroku Cloud:
1. (Hands-on) Deploy Flower Classification Model on Heroku
2. Heroku

7. Deploy Model on Google Cloud:
1. Section Introduction
2. Google cloud Introduction
3. (Hands-on) Upload Model on Google Cloud Storage
4. (Hands-on) Deploy model on Google app engine
5. (Hands-on) Deploy model on Google cloud Functions
6. (Hands-on) Deploy Model on Google AI cloud
7. Quiz

8. Deploy Model on AWS Lambda:
1. AWS Lambda ML Model Deployment

9. From Windows Machine:
1. AWS Lambda Hello World Function Part - 1
1.2 AWS Free Tier
2. AWS Lambda Introduction Hello World Part - 2
3. Model Packaging
4. Corrections
5. Upload Package to Amazon S3
6. Deploy Package on AWS Lambda and Test

10. From Linux Machine with serverless:
1. Section Introduction
2. Linux (UBUNTU) installation
3. Install Serverless Framework
4. Creating AWS user Credentials
5. Install Miniconda
6. Create serverless Project
7. Deploy artifacts on AWS Lambda and Test

11. Deploy Model with Docker on AWS Container:
1. Section Introduction
2. Docker Introduction
3. Docker Installation
4. Docker Basic Command
5. Setup Flower Deployment API on Docker Container
6. Run Prediction API - Container
7. Build Docker Image
8. Push Docker Image to Docker Hub
9. Run Docker Image on Amazon Container Service (ECS)

12. Bonus Lecture:
1. Bonus Lecture