In this week’s roundup, discover how ML is redefining app marketing and how the AI ecosystem is mapping the future of data science. Plus, learn more about the greatest female influencers in the data science world and why MLOps is critical to the future of your business. And finally, understand what role AI is playing in predicting river quality and weather.
By Sanjay Trisal, contributing writer for BrandEquity.com
Machine Learning (ML) is not just a buzzword. Its applications range from automating tasks to providing intelligent insights, which any industry can benefit from. But what does it deliver for digital and mobile marketers in particular?
By Fernando Lucini, contributing writer for InformationWeek.com
Over the past year, our reliance on technology to help us keep in touch, stay safe, work, shop, and more has hugely accelerated our use of data. Time and again, we’ve seen organizations use this vital resource to make informed decisions, often with life-saving consequences, in seconds.
By Monomita Chakraborty, a contributing writer for AnalyticsInsight.net
Data science has proven to be successful in addressing a wide range of real-world issues, and it is increasingly being used across industries to enable more intelligent and well-informed decision-making. There is a need for intelligent machines that can understand human actions and job habits as the use of computers for day-to-day business and personal operations expands. This pushes big data analytics and data science to the foreground.
By Dale Markowitz and Craig Wiley, contributing writers for Forbes.com
It used to be that training machine learning models—the “brains” of artificial intelligence (AI) systems that do everything from serving Google search results to targeting audiences with movies to turning people into cats for their court hearings—took a team of PhDs to get right. It’s astounding to see how quickly this has changed from the domain of experts to literal child’s play—or, at least, something a motivated techie can teach themselves online. Which means more and more enterprises are able to use machine learning to automate, predict, plan, and personalize their products and services.
By Tim Schley, contributing writer for News.PSU.edu
The difficulty and expense of collecting river water samples in remote areas has led to significant — and in some cases, decades-long — gaps in available water chemistry data, according to a Penn State-led team of researchers. The team is using artificial intelligence (AI) to predict water quality and fill the gaps in the data. Their efforts could lead to an improved understanding of how rivers react to human disturbances and climate change.
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