A demo may be the best way to make a concept real in the IT world. This blog describes one of a series of recorded demonstrations illustrating the use of VMware Tanzu on APEX Cloud Services as the platform for open-source based cloud-native applications leveraging containers with Kubernetes orchestration.
This week we’re showcasing an object detection application for an Advanced Driver-Assistance System (ADAS) monitoring road traffic in video sources that leverages several open-source projects to analyze streaming data using an artificial intelligence and machine learning (AI/ML) algorithm.
The base platform is VMware Tanzu running on APEX Private Cloud Service. APEX Private Cloud simplifies VMware cloud adoption as a platform for application modernization. It is based on Dell VxRail with VMware vSphere Enterprise Plus and vSAN Enterprise available as a 1- or 3-year subscription with hardware, software and services (deployment, rack integration, support and asset recovery) components included in a single monthly price. VMware Tanzu Basic Edition was added post-deployment to create the Container-as-a-Service (CaaS) platform with Kubernetes running integrated in the vSphere hypervisor.
Object detection in video sources requires managing streaming data for analysis, both real time and storing that data for later analysis. This demo includes the newly announced Dell ObjectScale object storage platform which was designed for Kubernetes as well as the innovative Dell Streaming Data Platform for ingesting, storing and analyzing continuously streaming data in real time.
The image recognition application leverages several open-source components:
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- Pravega software that implements a storage abstraction called a “stream” which is at the heart of the Streaming Data Platform.
- Apache Flink real time analytics engine for the object detection computations.
- Tensor Flow for the object detection model.
- Jupyter as the development environment for data flow and visualization.
The demo shows these components running in Tanzu Kubernetes Grid clusters to host the components of the object detection demo. It looks from the perspective of a data scientist who configures the projects and data flows in the Streaming Data Platform. Also, the Jupyter notebooks are configured to push the data into the Pravega stream and display the video with the object detection.
You can view the demo here.
Demos like these are a great way to see how the Dell Technologies components can be combined to create a modern application environment. Please view this demo and provide us some feedback on other demos you’d like to see in the future.
You can find more information on Dell Technologies solutions with VMware Tanzu here.
