Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
What is AWS Panorama?
3.
Advantages
4.
Use Cases
5.
Application Architecture
6.
Code Artifacts and Model Artifacts
7.
Running Locally and Deployment
8.
Monitoring
9.
Frequently Asked Questions
9.1.
Can AWS Panorama be used for traffic analysis?
9.2.
How does AWS Panorama handle privacy and security?
9.3.
What tools can be used to deploy AWS Panorama applications?
9.4.
How can developers monitor AWS Panorama applications?
9.5.
Is there a learning curve to use AWS Panorama?
10.
Conclusion
Last Updated: Mar 27, 2024
Easy

AWS Panorama

Author Juhi Pathak
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Advancements in technology have enabled computer vision to be an essential tool. AWS (Amazon Web Services) is at the forefront of empowering institutions. It has the ability to deploy computer vision models on the cloud. Some environments face challenges with limited internet. This makes it impractical to rely solely on the cloud. In such cases, AWS Panorama comes to the rescue. It provides a solution for deploying CV models on edge devices.

AWS Panorama

Let's explore what AWS Panorama is all about.

What is AWS Panorama?

  • AWS Panorama is a powerful service provided by AWS. It brings cutting-edge CV capabilities to on-premises camera networks. It allows organizations to seamlessly integrate CV apps at the edge. This enables real-time processing of video streams from network cameras. There is no need for constant internet connectivity.
     
  • The foundation of AWS Panorama lies in leveraging existing real-time streaming protocol (RTSP) cameras. This makes it easy to access valuable analytics directly from edge devices. By deploying CV apps on the edge, we can reduce latency. It ensures timely results for critical decision-making processes.
     
  • At the heart of AWS Panorama is the AWS Panorama Appliance, a compact and robust edge device. This appliance is optimized for ML workloads, allowing it to run multiple CV models. The result is high accuracy in analyzing video streams, even in demanding commercial settings. With a dust and liquid protection (IP-62) rating, the AWS Panorama is designed to withstand harsh environments.
     
  • AWS Panorama serves as a gateway between cloud and edge computing. It enables seamless integration with other AWS services. This makes application development and deployment streamlined and efficient. We can analyze traffic patterns, improve quality control, and collect data for model retraining with ease.
     
  • The service is flexible, accommodating various industries and use cases. In retail settings, AWS Panorama can track customer behavior. It can analyze foot traffic for improved customer experiences. In industrial environments, it can monitor safety compliance and detect potential hazards. For manufacturing processes, AWS Panorama can be utilized to identify defects and anomalies. This retains the highest quality control standards.
     
  • Overall, AWS Panorama empowers businesses to harness the power of CV and ML at the edge. By bringing intelligence closer to the data source, it provides businesses with a lot. With its seamless integration, AWS Panorama paves the way for a new era of edge-based CV apps.
Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Advantages

AWS Panorama offers several significant advantages that make it a game-changer for businesses.

Advantage
  • Real-Time Processing: AWS Panorama enables real-time processing of video streams from on-premises cameras. It eliminates the need to send data to the cloud for analysis. This reduces latency and ensures timely insights for critical decision-making processes.
     
  • Edge-Based Intelligence: By deploying CV apps at the edge, AWS Panorama brings intelligence closer to the data source. This allows businesses to operate autonomously, even in environments with limited networks. It ensures continuous usage and efficiency.
     
  • Seamless Integration: AWS Panorama seamlessly integrates with existing RTSP network cameras. This makes it easy to leverage their current camera infrastructure without additional investments.
     
  • High Accuracy and Efficiency: The AWS Panorama Appliance is optimized for ML workloads. It can run multiple computer vision models with high accuracy and efficiency. This ensures reliable insights from video streams.
     
  • Flexible Use Cases: AWS Panorama caters to a wide range of industries and use cases. From retail analytics to industrial safety monitoring, we can get to know all. Businesses can tailor computer vision applications to suit their specific needs.
     
  • Enhanced Privacy and Security: With AWS Panorama, data remains on-premises, providing enhanced privacy for sensitive information.
     
  • Integration with AWS Services: AWS Panorama seamlessly integrates with other AWS services. This simplifies application development and deployment. This allows businesses to leverage a wide array of tools to enhance their CV apps.
     
  • Dust and Liquid Protection: The AWS Panorama Appliance comes with a robust dust and liquid protection (IP-62) rating. It is suitable for demanding commercial and industrial environments.

Use Cases

AWS Panorama offers a versatile array of use cases across industries. It harnesses the power of CV at the edge to drive innovation.

  • Retail Analytics: In the retail sector, it can analyze customer foot traffic within stores. It enables retailers to gain insights into preferences. It optimizes store layouts, and enhances customer experiences.
     
  • Industrial Safety Monitoring: AWS Panorama can monitor industrial sites to ensure compliance with safety rules. It can detect potential hazards, and risky behaviors. This enables proactive safety and mitigating potential risks.
     
  • Quality Control in Manufacturing: It can monitor production lines to identify defects in products. It enables real-time quality control. This ensures that products meet the highest standards.
     
  • Traffic Management: For smart cities, it can analyze traffic patterns and congestion at crossroads. It provides data for optimizing traffic flow, and reducing congestion.
     
  • Surveillance and Security: AWS Panorama can enhance security in various settings. It can detect suspicious activities and trigger alerts for quick response.
     
  • Healthcare Applications: It can assist in patient monitoring and fall detection for the elderly. It can be used for contactless temperature screening.
     
  • Agriculture and Farming: AWS Panorama can monitor crops and livestock. It can detect diseases, pests, and crop health. It aids farmers in making data-driven decisions to optimize crop yield.
     
  • Autonomous Vehicles: It can work with autonomous vehicles for real-time object detection. It implements navigation, enhancing safety in driving systems.

These use cases showcase the versatile nature of AWS Panorama across industries.

Application Architecture

The application architecture of AWS Panorama is designed to facilitate seamless integration and efficient processing of computer vision applications at the edge. These apps are defined as graphs comprising nodes and edges. Each represents different components and their interactions.

Nodes in the application architecture can include camera streams, code, models, and output. Camera streams act as input sources, feeding real-time video data to the application. The code node is responsible for processing each image from the video stream. It preprocesses it, runs the model, annotates the results, and produces CloudWatch logs and metrics. The model node is called by the code node to perform inference on the images.

Application architecture

Edges connect nodes and define the flow of data within the application. For instance, an edge between the camera stream and code nodes represents the input data flow. An edge between the code and output nodes represents the annotated result's output.

To deploy an AWS Panorama app, developers can use the AWS Panorama Samples repository. It provides notebooks for testing and deploying apps or via the web console. AWS Panorama integrates seamlessly with other AWS services. It streamlines app development and allows businesses to leverage the full suite of AWS tools.

Overall, this application architecture empowers businesses with edge-based computer vision.

Code Artifacts and Model Artifacts

In AWS Panorama, code artifacts and model artifacts are essential components.

Code artifacts are defined as Docker container images responsible for processing data. These containers encapsulate the code necessary for various tasks. AWS Panorama uses the panorama-cli tool to package these into Docker images. The code node in the application architecture executes the code artifacts to analyze the video stream.

Model artifacts are the ML models used for computer vision tasks. AWS Panorama supports frameworks such as KerasTensorFlow, and Torch. These models need to be compiled in a format expected by Amazon SageMaker Neo. Model artifacts can be run on the image data. They provide predictions for further analysis.

Together, code artifacts and model artifacts form the backbone of AWS Panorama apps. This powerful combination harnesses the potential of computer vision. This transforms raw video data into driving innovation across various industries and use cases.

Running Locally and Deployment

Running AWS Panorama applications locally and deploying them to edge devices are crucial steps. To run an AWS Panorama app, developers can use the AWS Panorama Samples repository. It provides helpful notebooks for testing and development. These notebooks allow users to simulate the deployment environment. We can assess how the model is converted. We can check the performance of the graph and code on sample videos.

For deployment, there are two options: via the web console or using the panorama-cli. The web console provides an intuitive interface to guide users through the deployment process. Developers can use the panorama-cli, a command-line interface, to build containers. Before deployment, developers must define the application manifest, specifying the nodes.

Whether deploying via the web console, developers should keep their manifest file ready. They should utilize panorama-cli for efficient container building and application packaging. Once deployed, the AWS Panorama application will process video streams in real-time.

Monitoring

Monitoring in AWS Panorama is essential for ensuring the smooth operation of CV apps. It provides broad monitoring to track the health of both the AWS Panorama Appliance and the deployed apps.

Monitoring can be done through the web console and CloudWatch, which collects and analyzes logs. The web console offers real-time insights into the status of the app and connected camera streams. This provides a quick overview of their useful state.

CloudWatch logs capture application-level logs for each node. It enables developers to monitor the execution of code and ML models. Device logs capture system-level information from the AWS Panorama Appliance. This helps in diagnosing any working issues.

AWS Panorama provides valuable metrics to assess resource use. These metrics enable users to identify bottlenecks, and fine-tune application parameters.

Monitoring empowers businesses to proactively address any potential issues. It ensures the continuous availability of computer vision applications. With real-time insights and detailed logs, developers can maintain the reliability and efficiency of their edge-based computer vision systems, enhancing overall operational efficiency and driving better business outcomes.

Frequently Asked Questions

Can AWS Panorama be used for traffic analysis?

Yes, AWS Panorama can analyze traffic patterns and congestion in various settings, making it valuable for traffic management and smart city initiatives.

How does AWS Panorama handle privacy and security?

AWS Panorama processes video streams and data on-premises, providing enhanced privacy and security for sensitive information.

What tools can be used to deploy AWS Panorama applications?

AWS Panorama offers two deployment options: via the web console or programmatically using the panorama-cli. Both methods enable efficient application deployment and management.

How can developers monitor AWS Panorama applications?

Monitoring in AWS Panorama can be done through the web console, which provides real-time insights, and CloudWatch logs and metrics, which capture application and device-level information.

Is there a learning curve to use AWS Panorama?

AWS Panorama is designed for ease of use, and developers can leverage the AWS Panorama Samples repository to explore and test applications before deploying them in production environments.

Conclusion

This article discussed AWS Panorama, an Amazon Web Services that brings real-time computer vision capabilities to edge devices. We learn its advantages, use cases, application architecture, etc. Alright! So now that we have learned about AWS Panorama, you can refer to other similar articles.

You may refer to our Guided Path on Code Ninjas Studios for enhancing your skill set on DSACompetitive ProgrammingSystem Design, etc. Check out essential interview questions, practice our available mock tests, look at the interview bundle for interview preparations, and so much more!

Happy Learning, Ninja!

Previous article
AWS Fault Injection Simulator
Next article
AWS Prometheus
Live masterclass