Deploy a Production Machine Learning model with AWS & React

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Deploy a Production Machine Learning model with AWS & React
Language: English | Size:2.41 GB
Genre:eLearning
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Files Included :

Spoiler

001 Course overview.mp4 (16.16 MB)
MP4
002 What we're going to build.mp4 (28.71 MB)
MP4
003 Introduction.mp4 (6.44 MB)
MP4
001 Setting Up IAM Policies.mp4 (79.98 MB)
MP4
002 Setting Up SageMaker.mp4 (45.63 MB)
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003 Launching our SageMaker notebook.mp4 (21.61 MB)
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001 Cost optimazation and other Tips.mp4 (41.43 MB)
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001 Get data from Kaggle Part 1.mp4 (65.18 MB)
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002 Get Data from Kaggle Part 2.mp4 (3.31 MB)
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003 Important.mp4 (4.98 MB)
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004 Visualizing images.mp4 (41.26 MB)
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005 Correction.mp4 (4.49 MB)
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006 Resizing images(Theory).mp4 (17.44 MB)
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007 Computer Vision Part 1.mp4 (13.22 MB)
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008 Computer Vision Part 2.mp4 (130.1 MB)
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009 Resizing our images(Coding).mp4 (44.7 MB)
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010 Check the resized images.mp4 (57.23 MB)
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011 Data Visualization.mp4 (14.1 MB)
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012 Creating our DataFrame for Visualization.mp4 (53.95 MB)
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013 Creating our Bar Graphs.mp4 (7.37 MB)
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014 Making our Graphs nicer.mp4 (15.53 MB)
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001 What are lst Files.mp4 (54.75 MB)
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002 Creating Pandas DataFrame for lst files.mp4 (36.87 MB)
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003 Creating our lst files.mp4 (17.02 MB)
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004 Upload images and lst files to S3.mp4 (48 MB)
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005 Correction and Verify Upload.mp4 (9.52 MB)
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006 Setting up our Estimator object for training.mp4 (35.04 MB)
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008 Setting up Hyperparameter Tuning.mp4 (27.54 MB)
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009 Setting up Hyperparameters ranges.mp4 (26.39 MB)
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010 Correction.mp4 (8.7 MB)
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011 Setting up our Training Job.mp4 (53.5 MB)
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012 Starting our Training Job.mp4 (11.46 MB)
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001 Evaluating our Training Job.mp4 (128.29 MB)
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002 Deploying our model locally.mp4 (46.5 MB)
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003 Getting our First Inference.mp4 (40.81 MB)
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004 Constructing our confusion matrix.mp4 (82.18 MB)
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005 Recall, Precision, F1 Score.mp4 (30.58 MB)
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006 Shutting down our Endpoint.mp4 (6.91 MB)
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001 Creating IAM Policy for our lambda function.mp4 (45.16 MB)
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002 Coding our Lambda function.mp4 (80.52 MB)
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003 Creating Our API Gateway.mp4 (39.68 MB)
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004 Adding Endpoint name to Lambda.mp4 (5.65 MB)
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005 Image shape for Inference.mp4 (4.57 MB)
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006 Testing Our Endpoint with Postman.mp4 (31.53 MB)
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007 Setting up Lambda Concurrency.mp4 (12.93 MB)
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002 Setting up our MongoDB database.mp4 (25.46 MB)
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003 Downloading source code from Github.mp4 (18.92 MB)
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004 Launching our web application locally.mp4 (82.61 MB)
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005 Set Axios URL to our Endpoint.mp4 (5.08 MB)
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006 MERN app walkthrough Part 1.mp4 (184.7 MB)
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007 Start your Endpoint.mp4 (8.58 MB)
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008 MERN app walkthrough Part 2.mp4 (79.39 MB)
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001 AutoScaling for our Endpoint Part 1.mp4 (28.04 MB)
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002 AutoScaling for our Endpoint Part 2.mp4 (51.25 MB)
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003 Securing our Endpoint Part 1.mp4 (33.92 MB)
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004 Securing our Endpoint Part 2.mp4 (4.77 MB)
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001 Creating our DigitalOcean account.mp4 (6.56 MB)
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002 Setting Up our DigitalOcean server.mp4 (11.24 MB)
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003 SSH-ing into our DigitalOcean droplet.mp4 (27.45 MB)
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004 Installing Node js and NPM to our droplet.mp4 (58.31 MB)
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005 Creating our Frontend and Backend repositories.mp4 (20.73 MB)
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006 Clone Repos from Github and Install Nginx.mp4 (73.56 MB)
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007 Create env Files and Setting up MongoDB.mp4 (41.27 MB)
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008 Starting our Backend.mp4 (29.83 MB)
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009 Running our Frontend.mp4 (11.31 MB)
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010 Changing IP addresses.mp4 (16.91 MB)
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011 Testing on random images from the Internet.mp4 (31.41 MB)
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001 Delete Amazon SageMaker Endpoint.mp4 (5.91 MB)
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002 Clean Up and Next Steps.mp4 (14.25 MB)
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Deploy a Production Machine Learning model with AWS & React.z01

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Deploy a Production Machine Learning model with AWS & React.z03

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[0dayddl]

Deploy a Production Machine Learning model with AWS & React
Last updated 7/2023
Duration: 5h 45m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.41 GB
Genre: eLearning | Language: English​

Build a Scalable and Secure, Deep Learning Image Classifier with SageMaker, Next.js, Node.js, MongoDB & DigitalOcean

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
JavaScript 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

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