This week, we learn about the benefits of artificial intelligence (AI) and machine learning (ML), and how AI is transforming content management. We examine how to take your ML investment from pilot to production and whether or not data quality can be improved and maintained by automation and machine learning. And, we highlight how predictive analytics can improve your peer-to-peer fundraising efforts.
Welcome to the Future – How AI is Transforming Content Management
by Uri Kogan, contributing writer for MarTechAdvisor.com
AI has the potential to transform many areas of your business, as we’ve highlighted in previous roundups. Content management is a key area where AI is beginning to have a significant impact on business. Specifically, it can greatly enrich your metadata by accelerating the creation and classification of metadata attributes.
A Path from Pilot to Machine Learning Production
by Jessica Davis, contributing writer for InformationWeek.com
A recent study indicates that only 47 percent of machine learning models are making the transition from a pilot into production. Organizations are adopting a few strategies to ensure their machine learning investments move forward. One of these strategies is through implementing an MLOps practice. Learn what it is and how it’s helping.
Getting Serious About Peer-to-Peer Fundraising Data with Predictive Analytics
by Shana Masterson, contributing writer for NonProfitPro.com
When it comes to peer-to-peer fundraising, most of the analytics that people use focus predominantly on what’s happened in the past. This tells us nothing about what might happen in the future. Predictive analytics can help. A predictive approach helps nonprofits be better stewards of donor and fundraiser dollars, allowing for the use of data-driven predictions to lower costs, increase participation, and grow revenue.
The Benefits of AI and Machine Learning
by Prof Rose Luckin, Anthony Seldon and Priya Lakhani for TheGuardian.com
There are highly beneficial applications of machine learning in education. It enables personalized and individualized learning support for students. It can identify students’ specific needs so that they can receive targeted aid. When coupled with human-driven processes and skills, machine learning in education can inform strategy and decision making for the best possible support for students.
Can We Automate Data Quality To Support Artificial Intelligence and Machine Learning?
by Clint Hook, contributing writer for Information-Age.com
The success of AI and ML algorithms is only as good as their foundation—specifically, quality data. In order to ensure organizations have high-quality data, they must have a robust data strategy. This article examines whether or not data quality can be improved and maintained by automation and machine learning itself.
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