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

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Deploy a Production Machine Learning model with AWS & React

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.