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

Created by: Patrik Szepesi
Machine Learning Engineer

What Will I 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

Target audience

• 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