Lessons on Wi-Fi: Your Company's Biggest Security Gap

As an example of exploiting the synergy between AI and software engineering, the field of intelligent software engineering has emerged with various advances in recent years. Such a field broadly addresses issues on intelligent [software engineering] and [intelligence software] engineering. The former, intelligent [software engineering], focuses on instilling intelligence in approaches developed to address various software engineering tasks to accomplish high effectiveness and efficiency.

AI is a red-hot topic, even overhyped, some say. We will show why the latter group may well be right–about general AI, anyway. One thing’s for sure: we’re not going to have personal AI assistants anytime soon. Narrow AI applications–that is, practical, focused ones–are a different matter altogether. We believe such applications in business will deliver on their promise and revolutionize the world as we know it.

An organization’s success is defined by its digital transformation and customer-first approach. Most organizations have accrued hundreds to thousands of applications to operate, analyze and drive business-critical data forward. In most cases, the applications are built to perform a specific function and be reliable at one particular time and are not made to change and evolve. But to evolve it must compete in today’s business.

Deployment is a major challenge facing enterprise success in AI. In our last webinar, we discussed why many machine learning models don’t make it into production. On premise solutions face specific difficulties that we will discuss in this webinar. We will discuss best practices of enterprise machine learning, and how to get more of your models to production. While there are many solutions that help streamline the ML deployment process for cloud enterprises, few solutions exist for on premise enterprises. Join Aaron Schneider as he discusses ways to quickly deploy on your own servers or hardware at scale or on any cloud service.