How to Serialize - Deserialize model with scikit-learn & Deployment on Heroku, AWS Lambda, ECS, Docker and Google Cloud
What you'll learn
- Model Deployment Process
- Different option available for Model Deployment
- Deploy Scikit-learn, Tensorflow 2.0 Model with Flask Web Framework
- Deploy Model on Google cloud function, App engine
- Serve model through Google AI Platform
- Run Prediction API on Heroku Cloud
- Serialize and Deserialize model through Scikit-learn and Tensorflow
- Deploying model on Amazon AWS Lambda
- Install Flower prediction model with Docker
- Deploy Docker Container on Amazon Container Services (ECS)
- Basics of Python Programming
- Basic knowledge of Web development
Hello everyone, welcome to one of the most practical course on Machine learning and Deep learning model deployment production level.
What is model deployment :
Let's say you have a model after doing some rigorous training on your data set. But now what to do with this model. You have tested your model with testing data set that's fine. You got very good accuracy also with this model. But real test will come when live data will hit your model. So This course is about How to serialize your model and deployed on server.
After attending this course :
- you will be able to deploy a model on a cloud server.
- You will be ahead one step in a machine learning journey.
- You will be able to add one more machine learning skill in your resume.
1. Course Introduction
In this section I will teach you about what is model deployment basic idea about machine learning system design workflow and different deployment options are available at a cloud level.
2. Flask Crash course
In this section you will learn about crash course on flask for those of you who is not familiar with flask framework as we are going to deploy model with the help of this flask web development framework available in Python.
3. Model Deployment with Flask
In this section you will learn how to Serialize and Deserialize scikit-learn model and will deploy owner flask based Web services. For testing Web API we will use Postman API testing tool and Python requests module.
4. Serialize Deep Learning Tensorflow Model
In this section you will learn how to serialize and deserialize keras model on Fashion MNIST Dataset.
5. Deploy on Heroku cloud
In this section you will learn how to deploy already serialized flower classification data set model which we have created in a last section will deploy on Heroku cloud - Pass solution.
6. Deploy on Google cloud
In this section you will learn how to deploy model on different Google cloud services like Google Cloud function, Google app engine and Google managed AI cloud.
7. Deploy on Amazon AWS Lambda
In this section, you will learn how to deploy flower classification model on AWS lambda function.
8. Deploy on Amazon AWS ECS with Docker Container
In This section, we will see how to put application inside docker container and deploy it inside Amazon ECS (Elastic Container Services)
This course comes with 30 days money back guarantee. No question ask. So what are you waiting for just enroll it today.
I will see you inside class.
Who this course is for:
- Anyone who knows ML and want to move towards Model deployment
- Anyone who want to know how to put Machine Learning app into production