At our recent SNIA Cloud Storage Technologies (CSTI) webinar “Why Distributed Edge Data is the Future of AI” our expert speakers, Rita Wouhaybi and Heiko Ludwig, explained what’s new and different about edge data, highlighted use cases and phases of AI at the edge, covered Federated Learning, discussed privacy for edge AI, and provided an overview of the many other challenges and complexities being created by increasingly large AI models and algorithms. It was a fascinating session. If you missed it you can access it on-demand along with a PDF of the slides at the SNIA Educational Library.
Our live audience asked several interesting questions. Here are answers from our presenters.
Q. With the rise of large language models (LLMs) what role will edge AI play?
A. LLMs are very good at predicting events based on previous data, often referred to as next token in LLMs. Many edge use cases are also about prediction of the next event, e.g., a machine is going to go down, predicting an outage, a security breach on the network and so on. One of the challenges of applying LLMs to these use cases is to convert the data (tokens) into text with the right context.
Q. After you create an AI model how often do you need to update it?
A. That is very dependent on the dataset itself, the use case KPIs, and the techniques used (e.g., network backbone and architecture). It used to be where the data collection cycle is very long to collect data that includes outliers and rare events. We are moving away from this kind of development due to its cost and long time required. Instead, most customers start with a few data points and reiterate by updating their models more often. Such a strategy helps a faster return on the investment since you deploy a model as soon as it is good enough. Also, new techniques in AI such as unsupervised learning or even selective annotation can enable some use cases to get a model that is self-learning or at the least self-adaptable.
Q. Deploying AI is costly, what use cases tend to be cost effective?
A. Just like any technology, prices drop as scale increases. We are at an inflection point where we will see more use cases become feasible to develop and deploy. But yes, many use cases might not have an ROI. Typically, we recommend to start with use cases that are business critical or have potential of improving yield, quality, or both.
Q. Do you have any measurements about energy usage for edge AI? Wondering if there is an ecological argument for edge AI in addition to the others mentioned?
A. This is very good question and top of mind for many in the industry.There is not data yet to support sustainability claims, however running AI at the edge can provide more control and further refinement for making tradeoffs in relation to corporate goals including sustainability. Of course, compute at the edge reduces data transfer and the environmental impact of these functions.