AWS Cheat Sheet : AWS Panorama

What is AWS Panorama?

AWS Panorama is a machine learning (ML) service that enables you to run ML models on internet-connected devices locally. This allows you to analyze data at the edge of your network, where it is generated, without having to send it to the cloud.

Official website and definition —

AWS Panorama is a collection of machine learning (ML) devices and a software development kit (SDK) that brings CV to on-premises internet protocol (IP) cameras.

What are the benefits of using AWS Panorama?

• Reduced latency

AWS Panorama can reduce the latency of your ML applications by analyzing data at the edge of your network, close to where it is generated.

• Improved performance

AWS Panorama can improve the performance of your ML applications by offloading the processing of ML models from your devices to the cloud.

• Reduced costs

AWS Panorama can reduce the costs of your ML applications by reducing the amount of data that you need to send to the cloud.

What are the use cases for AWS Panorama?

• Predictive maintenance

AWS Panorama can be used to predict when equipment is likely to fail, so that you can take proactive action to prevent downtime.

• Quality control

AWS Panorama can be used to detect defects in products during the manufacturing process, so that you can fix them before they are shipped to customers.

• Customer behavior analysis

AWS Panorama can be used to analyze customer behavior data in stores or malls to improve customer service and marketing campaigns.

• Security monitoring

AWS Panorama can be used to monitor security data for threats and intrusions.

How to get started with AWS Panorama?

To get started with AWS Panorama we need to able to request and deploy an AWS Panorama appliance. Once deployed an AWS Panorama appliance, developers and data scientists can start creating and deploying ML models to it.

Here is a basic cheat sheet for using AWS Panorama:

1. Create an AWS account and deploy an AWS Panorama appliance.
2. Create a machine learning model using Amazon SageMaker or Amazon Rekognition.
3. Deploy your ML model to your AWS Panorama appliance.
4. Configure your AWS Panorama appliance to collect and analyze data from your devices.
5. View the results of your ML analysis in the AWS Panorama console.