Contrary to popular belief, data analytics is no longer only meant to take place in only the cloud or data center environments. Businesses of today are processing massive amounts of data closer to the edge of the network more than ever before. This transformation will lead to a new era of improving analytics, allowing for faster decision making and lower CAPEX/OPEX by a wide margin.
Here are five reasons to consider Edge AI:
- Network congestion – Since most approaches to Edge AI are based on finding efficient ways to send data to the cloud or a data center for processing. Under these conditions, your network can get congested quickly under backhaul traffic. Adding compression can help to minimize this issue, but the problem still remains. Unless you begin to do more processing at the edge, the problem just gets worse.
- Power – Most edge intensive environments are power-constrained. In order to get around these limitations, you need a purpose-built system with low power consumption. This allows you to minimize the resource impact on the operations that are the lifeblood of your organization.
- Space – Common edge environments are designed for something other than computer hardware infrastructure, such as doctor’s office, production factory or retail store. You need an edge solution that is space efficient in order to maximize limited resources.
- Security – Your data is confidential and in many cases it is the core of your competitive advantage. You need to keep it safe and secure, and the network used for your Edge AI needs to make security a top priority as well.
- Ease of Use – Creating a neural network for your analytics needs to easily scale up and down from training models to inference models. A light touch is nice, but zero touch is even better!
The way that you move processing power to the data is important. Don’t use a general purpose server if you want the best results for your edge analytics. Instead, think small and energy thrifty. Use a purpose-built, compact, low power server that includes the processing, including GPU/TPU, storage and highly-secure networking in a small form factor. The gap between data and processing will just get worse unless you move your analytics to the edge.
There are several benefits to this strategy, including:
- Reduced network traffic due to low backhaul requirements
- Reduced capital costs due to purpose-built, Edge AI hardware that is designed to be low cost with low power requirements to meet edge needs
- Data stays local to meet regulatory requirements or corporate guidelines
- Lower latency since you don’t need to wait for data to go to the cloud or data center for processing
- Easy to implement high-availability architecture since the a neural network is clustered by design
- Zero touch provisioning, great for large configurations
Cachengo has built a solution that fits all these requirements. The Bento is built around a 2.5” form factor purpose-built server that is packaged with eight of these server nodes into a 5×8” Box or 32 smart nodes into a 1 rack unit Pizza Box. Cachengo’s “point and click” management software divides the work among its neural network nodes in parallel to support your workload.
The nodes are connected with a high security zero trust network to keep your data safe. The operating system uses zero touch provisioning to make operating at scale easy. It is the best way to close the gap between your data and processing. You can get it as a service or an appliance, but either way you can efficiently and effectively manage your analytic workloads at the data source with Cachengo.
Find out more about Cachengo’s technology here.