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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Udemy

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English

language

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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

Screenshots

Deploy a Production Machine Learning model with AWS & React - Screenshot_01

Deploy a Production Machine Learning model with AWS & React - Screenshot_02

Deploy a Production Machine Learning model with AWS & React - Screenshot_03

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

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

Screenshots

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