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Why AWS Fargate?

We leverage Docker containers as the deployment artifact of both our internal services and cognitive engines. This gave us the flexibility to deploy and execute services in a reliable and portable way. Fargate on AWS turned out to be a perfect tool for orchestrating the dynamic nature of our deployments.

Fargate allows us to quickly scale Docker-based engines from zero to any desired number without having to worry about pre-provisioning capacity or bootstrapping and managing EC2 instances. We use Fargate both as a backend for quickly starting engine containers on demand and for the orchestration of services that need to always be running. It enables us to handle sudden bursts of real-time workloads with a consistent launch time. Fargate also allows our developers to get near-immediate feedback on deployments without having to manage any infrastructure or deal with downtime. The integration with Fargate makes this super simple.

Moving to Real Time

We designed a solution (shown below), in which media from a source, such as a mobile app, which “pushes” streams into our platform, or an IP camera feed, which is “pulled”, is streamed through a series of containerized engines, processing the data as it is ingested. Some engines, which we refer to as Stream Engines, work on raw media streams from start to finish. For all others, streams are decomposed into a series of objects, such as video frames or small audio/video chunks that can be processed in parallel by what we call Object Engines. An output stream of results from each engine in the pipeline is relayed back to our core platform or customer-facing applications via Veritone’s APIs.

Deployment and Orchestration

For processing real-time data, such as streaming video from a mobile device, we required the flexibility to deploy dynamic container configurations and often define new services (engines) on the fly. Stream Engines need to be launched on-demand to handle an incoming stream. Object Engines, on the other hand, are brought up and torn down in response to the amount of pending work in their respective queues.

EC2 instances typically require provisioning to be done in anticipation of incoming load and generally take too long to start in this case. We needed a way to quickly scale Docker containers on demand, and Fargate made this achievable with very little effort. aws cloud computing training in kerala

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