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

Deploy_a_Production_Machine_Learning_model_with_AWS___React.part1.rar - 995.0 MB

Deploy_a_Production_Machine_Learning_model_with_AWS___React.part2.rar - 995.0 MB

Deploy_a_Production_Machine_Learning_model_with_AWS___React.part3.rar - 478.9 MB