AWS DeepLens

AWS DeepLens for developers – The future of AI cameras & vision

AWS has made tremendous strides in the developer community by introducing the DeepLens camera attachment for deep learning synthesis. Essentially AWS DeepLens is an input capturing camera that uses deep learning and AI to understand the context behind captured visuals. You can use that information as raw data or create a chain of commands that follow as output-work. E.g. if you are developing a B2B logistics software then creating a camera code that tracks all license plates of trucks is an essential tool.

DeepLens can help you achieve a high-degree of input optimization as well, as you can start capturing information within minutes of launching. You can upload the command to the cloud and let DeepLens track information necessary as input.

You can choose your own deep learning model from the DeepLens pre-trained library or create your own ones via SageMaker. From finding album names for streaming websites, to determining smiles and frowns for security purposes, DeepLens can interact with its environment in real-time and share data.

Is AWS DeepLens developer friendly?

Yes, Amazon AWS DeepLens is highly developer friendly. Dr. Matt Wood, GM machine learning services at AWS, has designed DeepLens keeping in mind the ease of utility and scale via the cloud. It can be compared to the early Arduino boards for hardware enthusiasts. The DeepLens essentially becomes a platform on which companies and developers can build new things.

The platform, essentially, makes the world more orderly and data input more systematic. You don’t have to spend hours coding in the right variables and weights, as DeepLens will do it automatically. This speeds up the process of building an MVP or a true-form component inside a software base.

DeepLens makes machine learning less abstract and real world. This is a learning device. I can see developers inside the enterprise using machine learning in their own products. DeepLens allows developers to hone everyday skills and apply beyond in their organizations. With machine learning you need a lot of data. A video camera can capture things inside house, office and categorize them. There were a high number of devices have gone out to developers at re:Invent many with no machine learning experience.” – Dr. Matt

DeepLens can help developers that don’t have much training in Machine Learning as well, thereby reducing the need to pick up the coding skills necessary. They can leverage DeepLens, deploy on AWS cloud and create effective online solutions quicker.

Advantages of using DeepLens

  1. Developers can deploy solutions much faster when using DeepLens, as well as create more memorable experiences for customers and companies. When capturing input via the lens, developers also don’t need to know a lot about machine learning and code.
  2. AWS also has provided a vast resource in the form of community projects and inspiration. The platform can connect various developers working on synchronous projects to combine skillsets. The page also serves as a library for developers to get new ideas for their next projects.
  3. The pricing strategy for the DeepLens is also scalable across geographies. This means that more innovation will be coming up in deep parts of the world, with emphasis on capturing more information. Developers will be able to learn more about what innovations can be applied to which industries using DeepLens.
  4. SageMaker, Amazon’s proprietary software for DeepLens, is also a robust computing resource. You can use exiting libraries to accomplish your computing goals or create new ones from scratch.
  5. The technical strength of the DeepLens platform is also second to none, as it comes with AWS Greengrass Core and a version of MXNet. Users can also add their own frameworks like TensorFlow to reduce any redundancies or delay in deployment. The camera itself is a 4-megapixel one capable of 1080p HD video with a 2D microphone.

It comes preloaded with an Intel Atom processor (100 GFLOPS) that can run deep learning algorithms at 10 frames per second. The 8 GB of memory also makes sure that there are no processing delays while capturing data. It also comes with Wi-Fi, where you can use cloud computing for algorithms to run on internal hardware.

How can developers make the most of DeepLens?

Developers that haven’t experimented with visual Artificial Intelligence or deep learning can start the journey with DeepLens. It’s one of the most robust tools out there that can help you extend your existing code into the AI and ML sphere. DeepLens can be one of the best ways to introduce yourself to the possibilities of deep learning.

DeepLens also has a vast library of existing projects, templates and programs that can be reviewed by all developers. This can be a great way to make the most of the projects as they can review other works and brainstorm internally. They can introduce the tool to their companies and experiment with the technology early on. While the benefits of becoming an early-adopter are immense, the opportunities are even more abundant.

Since DeepLens is easy to customize and can be fully programmable, developers can start experimenting and tinkering to build their own models. Since visual input can become helpful in any project format, it’s a good tool to use early on. The programming environment is similar to users of AWS Lambda function and can easily adapt this project with others.

DeepLens also has a more comprehensive eco-system for advanced processing and computing. Developers can integrate DeepLens with Amazon Rekognition for advanced image analysis, SageMaker for existing training models, and Polly to introduce speech-enabled processing. You can also connect DeepLens to AWS IoT, SQS, SNS, DynamoDB, etc. The possibilities are endless for developers that have an innovative mindset.

What problems can be solved using DeepLens?

DeepLens can introduce new formats of input that reduce hindrances in verification, identification and processing. E.g. for projects that require manual input of data, a visual-input mode can allow for greater efficiency.

DeepLens can also create new areas of innovation, when thinking about new software to be introduced as a part of the existing offering. Companies can add DeepLens as a part of their scheduled release, as an added visual component to solution-oriented software.

Amazon has made it easy to use, which means that developers without prior experience of deep learning can start coding in its flexible system. With the vast libraries and coding parameters, developers can reduce redundancies and introduce more efficiency into their code.

Conclusion

Amazon DeepLens can become one of the most revolutionary devices to be released in the marketplace. For developers that require visual input and haven’t dug deep into ML, DeepLens provides the perfect package. The platform is built on multiple layers of deep learning and can interact with the external environment at an unprecedented rate.

Developers can essentially introduce vision to their existing projects, or launch new ones based on visual input mapping. They can also design new programs based on visual learning and coding using lens-vision. Since DeepLens comes with its own processing systems and can produce high-quality data, it’s the perfect platform on which to build upon. It’s one of the more developer-friendly builds offered by Amazon under it’s AWS ecosystem.

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