Download Build and Deploy a ML model to Production with AWS and React

What you would learn in Build and Deploy a ML model to Production with AWS and React course?

This class will utilize AWS Sagemaker, AWS API Gateway, Lambda, React.js, Node.js, Express.js MongoDB, and DigitalOcean to build an effective, secure, scalable, and robust production-ready enterprise-level image classifier. We will use the best practices and create IAM policies to start creating an environment that is secure within AWS. Then, we will use AWS built-in SageMaker Studio Notebooks. I'll demonstrate how to use any customized dataset you'd like. We will perform an exploratory analysis of our data using Matplotlib Seaborn, Pandas, and Numpy. After gathering valuable data about the data set, we will set up our Hyperparameter Tuning Job within AWS, in which I will demonstrate to you how to make use of GPU instances for speeding up the process of training. I will also teach you how to utilize multi GPU instances to train. Then, we will evaluate our training jobs and examine specific metrics like precision-recall, along with the F1 Score. Following the evaluation, we will deploy our deep-learning model on AWS using the AWS API Gateway and Lambda functions. Then, we will test our API using Postman and examine if we receive inference results. Once that's done, we will secure our API endpoints and set up autoscaling to prevent issues with latency. We will then create our web application that will access AWS API. AWS API. Then we will deploy our web application on DigitalOcean.

Course Content:

• Implement a production-ready solid, scalable, and secure Machine Learning application
• Configure Hyperparameter Tuning to be a part of AWS
• Find the most effective Hyperparameters using Bayesian search.
• Utilize Numpy, Matplotlib, Pandas, Seaborn in SageMaker
• Make use of AutoScaling to the deployment of Endpoints in AWS
• Make use of multiple instances of GPU instances to train in AWS
• Learn to use SageMaker Notebooks for any Machine Learning task within AWS
• Create an AWS API Gateway to connect our model over the internet.
• Secure AWS Endpoints that have limited IP address access
• Make use of any custom dataset you can find to help train
• Create IAM rules in AWS
• Configure Lambda concurrence in AWS
• Information Visualization with SageMaker
• Learn how to make maps with AWS
• Develop and then deploy a MongoDB Express, Nodejs, React/nextjs application to DigitalOcean
• Create an end-to-end machine learning pipeline that goes all from gathering data until deployment
• File Mode in contrast to Pipe Mode for deep learning models when you train models on AWS
• Utilize AWS' built-in Image Classifier
• Create models of deep learning using AWS SageMaker
• Learn how to access an AWS built-in algorithm using AWS ECR.
• CloudWatch Logs can be used to track training activities and make inferences.
• Examine machine learning models using the Confusion matrix and F1 scores Recall and Precision
• Access AWS endpoint through a deployed MERN web application running on DigitalOcean
• Create a beautiful website application
• Learn how you can combine AI Machine Learning and Machine Learning with Healthcare
• Configure Data Augmentation within AWS

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