{"id":9999,"date":"2026-08-10T10:13:48","date_gmt":"2026-08-10T10:13:48","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-deploy-a-production-machine-learning-model-with-aws-react-2024-12\/"},"modified":"2026-08-10T10:13:48","modified_gmt":"2026-08-10T10:13:48","slug":"udemy-deploy-a-production-machine-learning-model-with-aws-react-2024-12","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-deploy-a-production-machine-learning-model-with-aws-react-2024-12\/","title":{"rendered":"Udemy \u2013 Deploy a Production Machine Learning model with AWS & React 2024-12"},"content":{"rendered":"
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Descriptions<\/h2>\n

Deploy a Production Machine Learning model with AWS & React, 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\u2019 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.<\/p>\n

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.<\/p>\n

What you\u2019ll learn<\/h3>\n