Among the many possible applications of artificial intelligence, an impressive use case stands out: the visual recognition and analysis of materials and products in video streams and images. This process is the starting point for countless application possibilities in a wide variety of industries. The underlying principle is that pure analytics gains knowledge, however, but it is the artificial intelligence, that puts this knowledge to use. Similar to a human being, a machine must first build up a certain experience - i.e. knowledge - through countless experiments. This know-how is required to reliably identify different objects and states via video analytics.
Practice makes perfect!
In order to familiarize an AI with a large variety of objects, one of our analytics platform partners, Intelligent Data System GmbH from Frankfurt, has set up a neural training network on the NetApp/Cisco FlexPod architecture with the help of its complex event processing platform MOIRA.
Training an AI-supported video recognition can be challenging: machine learning is indispensable for the classification of objects and it goes hand in hand with large memory-intensive data models. In addition, machine learning models must be trained specifically for the respective use cases and require a sufficient amount of learning material. Due to this, it is often difficult to find successful models. However, if this undertaking is successful, a wide variety of objects can be identified.
In practice, this results in a perfect, flexible blueprint for a variety of different video analysis use cases. It can be used, for example, in quality assurance, in logistics for the recognition of goods, in video surveillance or for an automatic inventory solution via image recognition - to name just a few areas.
FlexPod: the perfect architecture for video analytics requirements
AI-based projects are only as successful as their trained "experience". This requires IT architectures that are extremely fast and facilitate this ongoing "training camp". In order to give neural networks the ideal training incentives, the training device must be as flexible and configurable as possible. Convergent systems like the FlexPod are ideally suited for both the local and the hybrid cloud approach.
NetApp and Cisco developed FlexPod with the goal of achieving maximum flexibility for exactly this type of application. It consists of high-performance, intelligent "cloud connected" NetApp flash memory, Cisco Systems UCS (Unified Computing System) with Nvidia GPUs and highly secure network components (Nexus switches). This power plant stores, manages, and secures the large video streams generated by the high-resolution cameras in NetApp data management in a space-saving manner. The data is then trained and analyzed against the models with the Nvidia V100 GPUs.
Benefits of video analytics with NetApp FlexPod:
Other FlexPod features such as validated designs, automation, adaptability or application spectrum are also an advantage. These aspects highlight the different parts of the FlexPod success story.
- Native hybrid cloud integration for storage, analytics, and archiving
- Extremely fast Flash/ NVME and Nvidia architectures
- Native protocol support for connectivity to a wide range of data repositories
- High automation and scalability from small to large
- Data persistence and availability important for container and Kubernetes concepts