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

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Deploy Machine Learning Models on GCP + AWS Lambda (Docker), available at $79.99, has an average rating of 4.5, with 54 lectures, 3 quizzes, based on 383 reviews, and has 3714 subscribers.

You will learn about 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) This course is ideal for individuals who are Anyone who knows ML and want to move towards Model deployment or Anyone who want to know how to put Machine Learning app into production It is particularly useful for Anyone who knows ML and want to move towards Model deployment or Anyone who want to know how to put Machine Learning app into production.

Enroll now: Deploy Machine Learning Models on GCP + AWS Lambda (Docker)

Summary

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

Price: $79.99

Average Rating: 4.5

Number of Lectures: 54

Number of Quizzes: 3

Number of Published Lectures: 53

Number of Published Quizzes: 3

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 56

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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)

Who Should Attend

  • Anyone who knows ML and want to move towards Model deployment
  • Anyone who want to know how to put Machine Learning app into production

Target Audiences

  • Anyone who knows ML and want to move towards Model deployment
  • Anyone who want to know how to put Machine Learning app into production

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.

What is going to cover in this course?

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 Serializeand Deserialize scikit-learn model and will deploy owner flaskbased Web services. For testing Web API we will use PostmanAPI 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 MNISTDataset.

5.  Deploy on Heroku cloud

In this section you will learn how to deploy already serialized flower classification data setmodel which we have created in a last section will deploy on Heroku cloud – Passsolution.

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.

Happy learning

Ankit Mistry

Course Curriculum

Chapter 1: Introduction

Lecture 1: Deployment Overview

Lecture 2: reviews

Lecture 3: Course – FAQ

Lecture 4: Join Online Classroom

Lecture 5: Machine Learning Workflow

Lecture 6: Different Model Deployment Option

Chapter 2: Code Download

Lecture 1: Code Download

Chapter 3: Flask Basics

Lecture 1: Introduction to Flask & Setup environment

Lecture 2: Download and Install Anaconda

Lecture 3: Create Virtual environment

Lecture 4: Install Library

Lecture 5: Spyder IDE

Lecture 6: Flask Introduction

Lecture 7: (Hands-on) Flask Hello World

Lecture 8: (Hands-on) Flask Web app – With parameter

Chapter 4: Deploying machine learning (Sci-kit Learn) model to Flask

Lecture 1: Section : Introduction

Lecture 2: Data Preparation & Create Model

Lecture 3: (Hands-on) Serialize & Deserialize Scikit-learn Model

Lecture 4: (Hands-on) Deploying model to Flask Web application

Lecture 5: Test Webservice through Postman +Python requests

Chapter 5: Model Serialization with Tensorflow 2.0

Lecture 1: Build Neural Network Model – keras (Tensorflow 2.0)

Lecture 2: (Hands-on) Serialize and Deserialize model

Chapter 6: ——– Deploy model on Heroku Cloud ——–

Lecture 1: (Hands-on) Deploy Flower Classification Model on Heroku

Chapter 7: ——- Deploy Model on Google Cloud ———-

Lecture 1: Section : Introduction

Lecture 2: Google cloud Introduction

Lecture 3: (Hands-on) Upload Model on Google Cloud Storage

Lecture 4: (Hands-on) Deploy model on Google app engine

Lecture 5: (Hands-on) Deploy model on Google cloud Functions

Lecture 6: (Hands-on) Deploy Model on Google AI cloud

Chapter 8: —– Deploy Model on AWS Lambda —–

Lecture 1: AWS Lambda : ML Model Deployment

Chapter 9: From Windows Machine

Lecture 1: AWS Lambda : Hello World Function Part – 1

Lecture 2: AWS Lambda Introduction : Hello World Part – 2

Lecture 3: Model Packaging

Lecture 4: Corrections

Lecture 5: Upload Package to Amazon S3

Lecture 6: Deploy Package on AWS Lambda and Test

Chapter 10: From Linux Machine with serverless

Lecture 1: Section : Introduction

Lecture 2: Linux (UBUNTU) installation

Lecture 3: Install Serverless Framework

Lecture 4: Creating AWS user Credentials

Lecture 5: Install Miniconda

Lecture 6: Create serverless Project

Lecture 7: Deploy artifacts on AWS Lambda and Test

Chapter 11: Deploy Model with Docker on AWS Container

Lecture 1: Section : Introduction

Lecture 2: Docker Introduction

Lecture 3: Docker Installation

Lecture 4: Docker Basic Command

Lecture 5: Setup Flower Deployment API on Docker Container

Lecture 6: Run Prediction API – Container

Lecture 7: Build Docker Image

Lecture 8: Push Docker Image to Docker Hub

Lecture 9: Run Docker Image on Amazon Container Service (ECS)

Chapter 12: Bonus Lecture

Lecture 1: Bonus Lecture

Instructors

Rating Distribution

  • 1 stars: 6 votes
  • 2 stars: 1 votes
  • 3 stars: 42 votes
  • 4 stars: 120 votes
  • 5 stars: 213 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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