• views: 50
• date: 1 November 2026
• posted by: AD-TEAM

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 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
jаvascript to deploy MERN apps

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

More Info





   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.