Deep learning models are wildly effective at solving complex problems using unstructured data. Unfortunately, these models are also (usually) big, slow to run, and resource intensive. This blog post explores options to reduce the size, inference time, and computational footprint for trained deep learning models in production.
It can be difficult to discuss AI opportunities with business customers as they probably don’t understand AI. After years of helping our clients overcome the myriad of challenges to identifying AI opportunities, I’ve developed a few hacks to help overcome them. Here, I’ll share my favorite one. I call it the “army of interns” hack.
Within the next five years, every large enterprise will begin an AI transformation. After helping dozens of Fortune 1000 companies to modernize for AI, I have seen stark differences between those that succeed and those that fail. In this blog post I describe the three common AI transformation patterns that don't work.
You’re going to run into extreme opinions on this topic. Scrum, Kanban, and the many variants have become so standard that today’s developers and product teams can’t imagine working differently. The “agile is best” zealotry has become so ingrained that proponents will reject any contrary opinions.
In this video Justin Pounders, Director of Machine Learning and AI Research at Prolego breaks down natural language generation (NLG) into its most basic components and describes how you can begin building out these components in your business.
Like most engineers, I hate tedious work. After I solve a problem once, I want a computer to take care of it whenever it pops up again. I try to automate everything, including machine learning projects. That’s why I love the idea of automatic machine learning (AutoML). Any innovation that makes data science projects easier frees us to work on more interesting problems.
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