Building and deploying artificial intelligence (AI) models at the network edge is a cumbersome process today. AI models must first be trained before they are released to power thousands (if not millions) of low-cost, low-power edge device hardware. And these models are often trained in the cloud or on other large-scale data center environments with static, pre-collected, pre-labeled data.
That's all well and good until the models are deployed in real-life operating environments where data and information are anything but static, pre-collected and pre-labeled! These models are suddenly expected to operate in these new environments and rapidly identify edge cases or anomalies. This causes chaos in the model and results in continued degradation of performance until the model becomes totally unusable.
New techniques and tools in AI deployment allow organizations to move their model development to a continuous learning cycle while drastically reducing operational costs and bandwidth requirements. The goal is to make it simpler for AI models to adapt to changing conditions at the edge - closer to the point where data is being created and consumed.
You’ve probably written a hundred abstracts in your day, but have you come up with a template that really seems to resonate? Go back through your past webinar inventory and see what events produced the most registrants. Sure – this will vary by topic but what got their attention initially was the description you wrote.
Paint a mental image of the benefits of attending your webinar. Often times this can be summarized in the title of your event. Your prospects may not even make it to the body of the message, so get your point across immediately. Capture their attention, pique their interest, and push them towards the desired action (i.e. signing up for your event). You have to make them focus and you have to do it fast. Using an active voice and bullet points is great way to do this.
Always add key takeaways. Something like this....In this session, you’ll learn about: