Big Data and ML Predictions 2018
Premise Big data everything — technology, products, markets, applications, and especially expectations — is being reset. 2018 will be the year that big data finally is embedded into business fabrics. The era of science experiments in big data is over. Through painful trial and failure, enterprises now have the experience required to begin extracting significant […]
CIO's Guide to AI – Leveraging Vendor Services
Mainstream businesses are aggressively pursuing AI/ML technologies. But the extreme skills shortage outside tech-centric companies is impeding meaningful progress. CIOs and LOB executives need to be prudent. Executives must tap what’s available in API-accessible cloud services as well as enterprise apps with embedded machine-learning.
CIO’s Guide to AI – Leveraging Vendor Services
Mainstream businesses are aggressively pursuing AI/ML technologies. But the extreme skills shortage outside tech-centric companies is impeding meaningful progress. CIOs and LOB executives need to be prudent. Executives must tap what’s available in API-accessible cloud services as well as enterprise apps with embedded machine-learning.
The CIO's Guide to AI – Leveraging Scarce Skills
Machine learning and AI are the focus of intensive experimentation, following similar experimentation with on-prem Hadoop-based big data projects. But if mainstream enterprises are going to leverage AI and ML widely over the next several years, they are going to have to pick their way carefully through an obstacle course. There just aren’t enough skilled data scientists to work at all levels of the ML and AI food chain.
The CIO’s Guide to AI – Leveraging Scarce Skills
Machine learning and AI are the focus of intensive experimentation, following similar experimentation with on-prem Hadoop-based big data projects. But if mainstream enterprises are going to leverage AI and ML widely over the next several years, they are going to have to pick their way carefully through an obstacle course. There just aren’t enough skilled data scientists to work at all levels of the ML and AI food chain.
Wikibon's 2018 Data Scientist and Edge Analytics Predictions
Premise Explosive growth in AI-enhanced capabilities will shift data science work from data scientists wrangling data and coding manual experiments to shepherding the output and results of intelligent software and likewise pushing advanced analytical work down the corporate food chain. At the same time, meg-AI initiatives like IBM Watson are finding themselves obsolete as their […]