Build an AWS Machine Learning Pipeline for Object Detection

Use AWS Step Functions + Sagemaker to Build a Scalable Production Ready Machine Learning Pipeline for Plastic Detection

What you'll learn
Learn how you can use Google's Open Images Dataset V7 to use any custom dataset you want
Create Sagemaker Domains
Upload and Stream data into you Sagemaker Environment
Learn how to set up secure IAM roles on AWS
Build a Production Ready Object detection Algorithm
Use Pandas, Numpy for Feature and Data Engineering
Understanding Object detection annotations
Visualising Images and Bounding Boxes with Matplotlib
Learn how Sagemaker's Elastic File System(EFS) works
Use AWS' built in Object detection detection algorithm with Transfer Learning
How to set up Transfer Learning with both VGG-16 and ResNet-50 in AWS
Learn how to save images to RecordIO format
Learn what RecordIO format is
Learn what .lst files are and why we need them with Object Detection in AWS
Learn how to do Data Augmentation for Object detection
Gain insights into how we can manipulate our input data with data augmentation
Learn AWS Pricing for SageMaker, Step Functions, Batch Transformation Jobs, Sagemaker EFS, and many more
Learn how to choose the ideal compute(Memory, vCPUs, GPUS and kernels) for your Sagemaker tasks
Learn how to install dependencies to a Sagemaker Notebook
Setup Hyperparameter Tuning Jobs in AWS
Set up Training Jobs in AWS
Learn how to Evaluate Object detection models with mAP(mean average precision) score
Set up Hyperparameter tuning jobs with Bayesian Search
Learn how you can configure Batch Size, Epochs, optimisers(Adam, RMSProp), Momentum, Early stopping, Weight decay, overfitting prevention and many more in AWS
Monitor a Training Job in Real time with Metrics
Use Cloudwatch to look at various logs
How to Test your model in a Sagemaker notebook
Learn what Batch Transformation is
Set up Batch Transformation Jobs
How to use Lambda functions
Saving outputs to S3 bucket
Prepare Training and Test Datasets
Data Engineering
How to build Complex Production Ready Machine Learning Pipelines with AWS Step Functions
Use any custom dataset to build an Object detection model
Use AWS Cloudformation with AWS Step Functions to set up a Pipeline
Learn how to use Prebuilt Pipelines to Configure to your own needs
Learn how you can Create any Custom Pipelines with Step Functions(with GUI as well)
Learn how to Integrate Lambda Functions with AWS Step Functions
Learn how to Create and Handle Asynchronous Machine Learning Pipelines
How to use Lambda to read and write from S3
AWS best practices
Using AWS EventBridge to setup CRON jobs to tell you Pipeline when to Run
Learn how to Create End-to-End Machine Learning Pipelines
Learn how to Use Sagemaker Notebooks in Production and Schedule Jobs with them
Learn Machine Learning Pipeline Design
Create a MERN stack web app to interact with our Machine Learning Pipeline
How to set up a production ready Mongodb database for our Web App
Learn how to use React, Nextjs, Mongodb, ExpressJs to build a web application
Create and Interact with JSON files
Put Convolutional Neural Networks into Production
Deep Learning Techniques
How to clean up an AWS account after you are done
Train Machine Learning models on AWS
How to use AWS' GPUs to speed up Machine Learning Training jobs
Learn what AWS Elastic Container Registry(ECS) is and how you can download Machine Learning Algorithms from it
AWS Security Best practices

Requirements
Laptop with Internet Access
AWS account
Knowledge of Python and basic Machine Learning
Spend 20-50 dollars on AWS if you want to follow along with me. Note that you can still follow along without having to pay any money

Description
Welcome to the ultimate course on creating a scalable, secure, complex machine learning pipeline with Sagemaker, Step Functions, and Lambda functions. In this course, we will cover all the necessary steps to create a robust and reliable machine learning pipeline, from data preprocessing to hyperparameter tuning for object detection.We will start by introducing you to the basics of AWS Sagemaker, a fully-managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning models quickly and easily. You will learn how to use Sagemaker to preprocess and prepare your data for machine learning, as well as how to build and train your own machine learning models using Sagemaker's built-in algorithms.Next, we will dive into AWS Step Functions, which allow you to coordinate and manage the different steps of your machine learning pipeline. You will learn how to create a scalable, secure, and robust machine learning pipeline using Step Functions, and how to use Lambda functions to trigger your pipeline's different steps.In addition, we will cover deep learning related topics, including how to use neural networks for object detection, and how to use hyperparameter tuning to optimize your machine learning models for different use cases.Finally, we will walk you through the creation of a web application that will interact with your machine learning pipeline. You will learn how to use React, Next.js, Express, and MongoDB to build a web app that will allow users to submit data to your pipeline, view the results, and track the progress of their jobs.By the end of this course, you will have a deep understanding of how to create a scalable, secure, complex machine learning pipeline using Sagemaker, Step Functions, and Lambda functions. You will also have the skills to build a web app that can interact with your pipeline, opening up new possibilities for how you can use your machine learning models to solve real-world problems.

Who this course is for:
For developers who want to take their machine learning skills to the next lever by being able to not only build machine learning models, but also incorporate them in a complex, secure production ready machine learning pipeline