Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
My goal is to identify images that have a person in them to limit the number of images someone has to browse when reviewing the security camera alarms (security cameras detect motion – so often you get images that are just wind motion in bushes, or headlights on a wall).
Every company is a digital company. No longer is it a question of if your product will become digital – as was the case with music, newspapers, TV, movies, etc. – it is a question of how the experience of your product (and your company) changes even if your product isn’t digitized.
eCommerce, digital marketing, social, CRM, and content technology and strategies are critical. You will need to invest in those technologies – but underpinning all of those technologies is data – that data is the currency of your digital transformation.
In part two of this series I discussed creating a serverless data collection and processing fabric for an IoT deployment. To recap, we’ve now reviewed the local devices and controller/gateway pattern for the security cameras deployed. We’ve also discussed the Amazon Web Services infrastructure deployed to collect, process and catalog the data generated by the security cameras.
In this post we will cover the creation of a serverless REST API.
In part one of this series I briefly discussed the purpose of the application to be built and reviewed the IoT local controller & gateway pattern I’ve deployed. To recap, I have a series of IP cameras deployed and configured to send (via FTP) images and videos to a central controller (RaspberryPI 3 Model B). The controller processes those files as they arrive and pushes them to Amazon S3. The code for the controller process can be found on GitHub.
In this post we will move on to the serverless processing of the videos when they arrive in S3.
… but vendors will fight you every step of the way.
Remember – every vendor wants to get as much of your data in their database as possible. And every data approach (Relational, Document, Key/Value Store and Graph) can be forced to do just about anything; and given the opportunity the vendor will tell you their solution is the right choice.
The challenge for the modern Enterprise Data Architect is maintaining a cogent point of view about assembling a polyglot solution to make each use case (or micro service) less complex, more scalable and easier improve/enrich over time.
However, practical illustrations of patterns and implementations are exceptionally hard to find. This series of posts will attempt to close that gap by providing both the purpose, design and implementation of a complete serverless application on Amazon Web Services.
Part 1 – The setup…
Every application needs a reason to exist – so before we dive into the patterns and implementation we should first discuss the purpose of the application.
Nest wants how much for cloud storage and playback?
I have 14 security cameras deployed, each captures video and still images when motion is detected. These videos and images are stored on premises – but getting them to “the cloud” is a must have – after all if someone breaks in and takes the drive they are stored on all the evidence is gone.
If I were to swap all of the cameras out for Nest cameras cloud storage and playback would cost $2250/year – clearly this can be done cheaper… so…