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

4.72 (87 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Screenshots

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