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Deploy a Production Machine Learning model with AWS & React

Deploy a Production Machine Learning model with AWS & React

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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What you'll 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 deploy 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


  • Any laptop
  • Some Python and Machine Learning Knowledge
  • about 15-40 dollars for using AWS resources(Optional, only applies if you follow along with me)


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.

Who this course is for:

  • Those with some ML experience who are hoping to take their skills to the next step by being able to deploy their deep learning models to production
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