This week, take a dive into what automated machine learning (AutoML) is and what the different types are. Understand the risks and what might go wrong with artificial intelligence (AI) and AutoML, and how you can learn to improve your application of the technology. Learn the three steps to building a better attribution model. And finally, find out how to transform your customer experience (CX) through AI and analytics.
AutoML in Practice
by Daniel Gutierrez, contributing writer for InsideBigData.com
View this compelling presentation, originally presented by Danny D. Leybzon from Qubole at a recent LA Machine Learning meetup. The information provides a broad view of automated machine learning (AutoML), ranging from simple hyperparameter optimization all the way to pipeline automation. Leybzon also introduces concrete examples of the types of AutoML and so much more.
The Risks of AutoML and How to Avoid Them
by Ahmed Abbasi, Brent Kitchens, and Faizan Ahmad for HBR.org
Big data hubris is the fallacy that inductive reasoning fueled by copious amounts of data can supplant traditional, deductive analysis guided by human hypotheses. This applies to AI as well, especially when coupled with AutoML. A project highlighted in this article examines the efficacy of detecting adverse events from large quantities of digital user-generated content. The team employed an augmented machine learning approach (augML). Learn the risks of AutoML, what AugML is, and what to consider when looking to enhance your machine learning capabilities in this informative article.
Why It’s Nice to Know What Can Go Wrong with AI
by James M. Connolly, contributing writer for InformationWeek.com
When new technologies are adopted, there are always growing pains. There are issues and bad things that happen. This is no new phenomenon, the author highlights examples of issues that happened with now-widely-adopted technologies when they were in the early stages. When it comes to applying this approach to AI, the key is to learn early and respond quickly to improve the technology.
3 Steps to Building a Better Marketing Attribution Model
by Benoit Grouchko, contributing writer for MarTechAdvisor.com
Building a great attribution model isn’t easy. Marketers continue to struggle to understand if marketing spend is truly impacting the bottom line. There’s often a missing link between online marketing and in-store purchases. This article covers the challenges behind attribution and provides three steps to build a better marketing attribution model to get the most out of digital marketing spend.
Transforming CX Through AI & Analytics
by Chris Kuehl, contributing writer for MarTechAdvisor.com
With such a competitive landscape, CX is more important than ever before. In fact, nearly a third of customers will stop doing business with a brand after only one bad experience and nearly two-thirds indicate experience as being important when deciding on a purchase. Customer experience is a critical asset in maintaining loyalty for brands, and AI technology combined with data analytics may be the key to keeping consumers engaged. Here are three ways your organization can transform CX with AI and analytics.
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