AWS, Cloud Computing

3 Mins Read

Accelerate Your Computer Vision Projects with AWS DeepLens

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The Internet of Things (IoT) has revolutionized our interactions with our surroundings. It allows us to collect data from various devices and sensors, which can be harnessed to make informed decisions. One essential aspect of IoT is computer vision, which empowers devices to ‘see’ and interpret the world around them. AWS DeepLens is a powerful tool that seamlessly integrates IoT and computer vision, enabling you to bring vision to your IoT applications. In this comprehensive guide, we’ll dive into the world of AWS DeepLens and show you how to get started.

Introduction to AWS DeepLens

AWS DeepLens is a unique combination of hardware and software provided by Amazon Web Services. It’s a deep learning-enabled video camera designed for developers, enabling them to build and deploy computer vision models in real-world scenarios. This smart camera has features like GPU acceleration, pre-trained models, and integration with popular machine-learning frameworks like TensorFlow and Apache MXNet.

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Key Features of AWS DeepLens

  • Built-In Camera: AWS DeepLens has a high-definition camera for real-time video capture. This camera is essential for capturing visual data that can be used in computer vision projects.
  • Intel Integrated GPU: It features an Intel Atom processor with an integrated GPU (Graphics Processing Unit). The GPU accelerates deep learning inference, making real-time video feed analysis possible.
  • Integrated with AWS Services: AWS DeepLens integrates with various AWS services, including AWS Lambda, Amazon S3, Amazon DynamoDB, and Amazon Rekognition. This makes building and deploying AI projects easy by leveraging these cloud services.
  • Pre-Trained Model: AWS DeepLens comes with a selection of pre-trained deep learning models ready to use out of the box. These models cover a range of computer vision tasks, such as object recognition and face detection.

Getting Started with AWS DeepLens


Source: AWS 

Fig: This image shows the Front and back of AWS DeepLens Hardware

  1. Hardware Setup
  • Unboxing: Carefully unbox your AWS DeepLens device, ensuring you have all the components, including the camera, power supply, and a mounting bracket.
  • Connect to Wi-Fi: Power on the device and follow the on-screen instructions to connect it to your Wi-Fi network.
  • Update Firmware: Ensure your AWS DeepLens device has the latest firmware by checking for updates in the AWS DeepLens console.
  1. AWS DeepLens Console
  • Create an AWS Account: If you don’t already have one, sign up for an AWS account.
  • Access the DeepLens Console: Log in to the AWS Management Console and navigate to the AWS DeepLens console.
  • Create a Project: Create a new project in the AWS DeepLens console to get started. A project is the container for your machine-learning model.
  1. Model Development and Deployment
  • Choose a Framework: Decide on the machine learning framework you want to use, either TensorFlow or Apache MXNet, and set up your development environment.
  • Train Your Model: You can use your dataset or the AWS DeepLens Model Zoo to train your custom model.
  • Deploy the Model: Deploy your model to your AWS DeepLens device from the AWS DeepLens console.
  1. Building IoT Applications
  • Create Lambda Functions: Use AWS Lambda to build serverless functions that process data from your DeepLens device.
  • Integrate with AWS IoT Core: Connect AWS DeepLens to AWS IoT Core to manage your IoT devices and data.
  • Visualize Data: Use Amazon SageMaker to visualize and analyze the data collected from your AWS DeepLens device.

Real-World Example: Healthcare Monitoring and Diagnostics

Problem: A healthcare provider wanted to improve patient care and monitoring in a hospital setting. They needed a solution to monitor patient vital signs and provide early warnings of potential health issues, especially for patients in critical care.

Solution with AWS DeepLens:

  • The healthcare provider placed AWS DeepLens cameras in patient rooms, particularly in the intensive care unit (ICU). These cameras were equipped with computer vision capabilities to monitor patients’ vital signs, such as heart rate, respiratory rate, and body temperature.
  • AWS DeepLens was used to recognize patient faces and assess their emotional states. This helped gauge patient comfort and detect signs of distress.
  • The cameras were programmed to detect any sudden falls or unusual movements by patients. Alerts were sent to healthcare staff in real-time to respond quickly to emergencies.
  • AWS DeepLens assisted in wound care by providing close-up images of surgical wounds and monitoring for signs of infection or complications.
  • Data from AWS DeepLens were integrated with Electronic Health Records (EHR) systems, creating a comprehensive patient history. This data helped in early diagnostics and creating treatment plans.


AWS DeepLens is like a super-smart camera that can see and understand things in the real world. It can be used in many ways, like making shopping easier, keeping patients safe in the hospital, and even helping farmers with their crops.

It uses special technology to do all these things and more, and it’s part of Amazon’s cloud services. So, it’s not just a camera; it’s a whole system that can make our lives better and more efficient.

Drop a query if you have any questions regarding AWS DeepLens and we will get back to you quickly.

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1. How much does the AWS DeepLens device cost?

ANS: – The cost of the AWS DeepLens device varies depending on factors like the region and any special offers or discounts that may be available at the time of purchase. You can find the current pricing on the AWS DeepLens website.

2. Does AWS DeepLens come with Amazon Alexa?

ANS: – No, AWS DeepLens does not come with Amazon Alexa built into the device. AWS DeepLens is primarily designed for computer vision and machine learning applications.

WRITTEN BY Chamarthi Lavanya

Lavanya Chamarthi is working as a Research Associate at CloudThat. She is a part of the Kubernetes vertical, and she is interested in researching and learning new technologies in Cloud and DevOps.



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