Deploy a Production Machine Learning model with AWS & React

Build a Scalable and Secure, Deep Learning Image Classifier with SageMaker, Next.js, Node.js, MongoDB & DigitalOcean

4.65 (41 reviews)
Udemy
platform
English
language
Other
category
instructor
244
students
6 hours
content
Jul 2022
last update
$69.99
regular price

What you will learn

Deploy a production ready robust, scalable, secure Machine Learning application

Set up Hyperparameter Tuning in AWS

Find the best Hyperparameters with Bayesian search

Use Matplotlib, Numpy, Pandas, Seaborn in SageMaker

Use AutoScaling for our deployed Endpoints in AWS

Use multi-instance GPU instance for training in AWS

Learn how to use SageMaker Notebooks for any Machine Learning task in AWS

Set up AWS API Gateway to deploy our model to the internet

Secure AWS Endpoints with limited IP address access

Use any custom dataset for training

Set up IAM policies in AWS

Set up Lambda concurrency in AWS

Data Visualization in SageMaker

Learn how to do MLOps in AWS

Build and deploy a MongoDB, Express, Nodejs, React/nextjs application to DigitalOcean

Create an end to end machine learning pipeline all the way from gathering data to deployment

File Mode vs Pipe Mode when training deep learning models on AWS

Use AWS' built in Image Classifier

Create deep learning models with AWS SageMaker

Learn how to access any AWS built in algorithm from AWS ECR

Use CloudWatch logs to monitor training jobs and inferences

Analyze machine learning models with Confusion matrix, F1 score, Recall, and Precision

Access AWS endpoint through a deployed MERN web application running on DigitalOcean

Build a beautiful web application

Learn how to combine AI and Machine Learning with Healthcare

Set up Data Augmentation in AWS

Machine Learning with Python

JavaScript to deploy MERN apps

Description

In this course we are going to use AWS Sagemaker, AWS API Gateway, Lambda, React.js, Node.js, Express.js MongoDB and DigitalOcean to create a secure, scalable, and robust production ready enterprise level image classifier. We will be using best practices and setting up IAM policies to first create a secure environment in AWS. Then we will be using AWS' built in SageMaker Studio Notebooks where I am going to show you guys how you can use any custom dataset you want. We will perfrom Exploratory data analysis on our dataset with Matplotlib, Seaborn, Pandas and Numpy. After getting insightful information about dataset we will set up our Hyperparameter Tuning Job in AWS where I will show you guys how to use GPU instances to speed up training and I will even show you guys how to use multi GPU instance training. We will then evaluate our training jobs, and look at some metrics such as Precision, Recall and F1 Score. Upon evaluation we will deploy our deep learning model on AWS with the help of AWS API Gateway and Lambda functions. We will then test our API with Postman, and see if we get inference results. After that is completed we will secure our endpoints and set up autoscaling to prevent latency issues. Finally we will build our web application which will have access to the AWS API. After that we will deploy our web application to DigitalOcean.



Screenshots

Deploy a Production Machine Learning model with AWS & React - Screenshot_01Deploy a Production Machine Learning model with AWS & React - Screenshot_02Deploy a Production Machine Learning model with AWS & React - Screenshot_03Deploy a Production Machine Learning model with AWS & React - Screenshot_04

Content

Intro

Course overview
What we're going to build
Introduction

Setting up SageMaker

Setting Up IAM Policies
Setting Up SageMaker
Launching our SageMaker notebook

Quick Tips before getting our Data

Cost optimazation and other Tips

Exploratory data analysis

Get data from Kaggle Part 1
Get Data from Kaggle Part 2
Important
Visualizing images
Correction
Resizing images(Theory)
Computer Vision Part 1
Computer Vision Part 2
Resizing our images(Coding)
Check the resized images
Data Visualization
Creating our DataFrame for Visualization
Creating our Bar Graphs
Making our Graphs nicer

Setting up our AWS training Job

What are .lst Files
Creating Pandas DataFrame for .lst files
Creating our .lst files
Upload images and .lst files to S3
Correction and Verify Upload
Setting up our Estimator object for training
IMPORTANT, Correction for Max Runs
Setting up Hyperparameter Tuning
Setting up Hyperparameters ranges
Correction
Setting up our Training Job
Starting our Training Job

Evaluating and Testing our Trained model

Evaluating our Training Job
Deploying our model locally
Getting our First Inference
Constructing our confusion matrix
Recall, Precision, F1 Score
Shutting down our Endpoint

Deploying our model Endpoint to Amazon API Gateway

Creating IAM Policy for our lambda function
Coding our Lambda function
Creating Our API Gateway
Adding Endpoint name to Lambda
Image shape for Inference
Testing Our Endpoint with Postman
Setting up Lambda Concurrency

Creating our web application

Source Code
Setting up our MongoDB database
Downloading source code from Github
Launching our web application locally
Set Axios URL to our Endpoint
MERN app walkthrough Part 1
Start your Endpoint
MERN app walkthrough Part 2

Scalability and Security

AutoScaling for our Endpoint Part 1
AutoScaling for our Endpoint Part 2
Securing our Endpoint Part 1
Securing our Endpoint Part 2

Deploying our web application to DigitalOcean

Creating our DigitalOcean account
Setting Up our DigitalOcean server
SSH-ing into our DigitalOcean droplet
Installing Node.js and NPM to our droplet
Creating our Frontend and Backend repositories
Clone Repos from Github and Install Nginx
Create env Files and Setting up MongoDB
Starting our Backend
Running our Frontend
Changing IP addresses
Testing on random images from the Internet

Outro

Delete Amazon SageMaker Endpoint
Clean Up and Next Steps
Delete Elastic File System

Reviews

Kiril
July 3, 2022
Beyond expectations, this is full machine learning pipeline in 7 hours of video yet it is extremely detailed with every single line of code covered. Wow!!
Priyabrata
June 9, 2022
Brilliant !!!!!.... Exactly what I expected... The duration of the lessons are kept short so that they don't look boring and stretchy. Explanations are great and to the point. Highly recommended.
Taniya
June 5, 2022
Nice course . This course will definately give you good kickstart towards your journey of sagemaker . #Suggestion Material is very good but might be some where difficult when you deal with text or any other kind of dataset ,deployment of this couse is performed on image classification dataset.
John
March 8, 2022
This was exactly what I was looking for. I have been stumped for so long trying to build ML Models through AWS but this course absolutely nocked it out of the park. I would highly recommend this course!
Joe
March 8, 2022
Probably the most comprehensive and easy to follow course I have ever taken on Udemy. Highly recommend for anyone wanting to learn and build ML Models.
Réka
March 8, 2022
challenging and informative, but worth the effort. tutor is easy to understand, the course is well structured. glad I found an up to date ML course! so far it has been very insightful, the exercises are broken down into understandable, clear steps. good job!
Henry
March 7, 2022
This is the course if you want to deploy a production machine learning model, but also build and deploy a Full Stack MERN web application for it. Can't wait for his other courses
Máté
March 7, 2022
The hands on experience makes it so much better than just reading it off of a powerpoint. Everything is explained clearly.

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4473162
udemy ID
1/3/2022
course created date
6/16/2022
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