Udemy.Deploy.a.Production.Machine.Learning.model.with.AWS.and.React.BOOKWARE-BLZiSO

Section
Appz
Group
BLZiSO
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2,11 GB
Files
48
Date
2026-11-01

NFO

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     ▀██▄▄▄▄▄▄▄▄▄▄▄▄▄▄███▀  ▀██▄▄▀ ░ .prouldy presents. ▄  ▀▄▄██▀ ▌   ▄ ▓    ▐█
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            █ Deploy a Production Machine Learning      █            ▀▀███▀▀
            █ model with AWS and React                  █            ▀▀███▀▀
          ▀▀█▀█▀▀                                  ▀▀▀▀▀█▀▀ ▀           █
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           ▀█ release date.: 01/11/26  disks........: 01                ▓
            █ supplier.....: Bill      os...........: Win               █
            ▓ cracker......: Bill      protection...: Bill            ▄ █
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       ▀▄▄▄   ▄▄█▓   ▀  .;Program info;.  ░                          ▀ 
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	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.
	https://www.udemy.com/course/build-and-deploy-a-ml-model-to-production-with-aws-and-react
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            ░    All Groups which are actively and courageously work on █    
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            █    we wish them the best in real life and thank them for  █
            █    for all their great work they have done for blizzard...█
            █    we'll never forget you!! our fallen heroes:            █
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                         if we need you then we will contact you!       █
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        Software worth using is worth buying.. keep this in mind!       █
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       ▀▄▄▄   ▄▄█▓   ▀ .; Artwork updated 31.01.2005 ░               ▀    
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b-udyedlpoyarpocduitonachmineelarningodlewthiwasndaercat.r4411,78 MB
b-udyedlpoyarpocduitonachmineelarningodlewthiwasndaercat.rar47,68 MB
b-udyedlpoyarpocduitonachmineelarningodlewthiwasndaercat.sfv3,19 KB
blziso.nfo12,05 KB