The gap is widening between those with a handle on their AI strategy and those only scratching the surface of its potential.
In recent years, search engines have led the way in natural language processing (NLP) and the benefits it stands to bring across business landscapes. Focused around machine learning capabilities, applications like Google’s BERT work to process natural language without human input. Used right, such systems can remove redundant work processes for faster and more efficient handling, analysis, and translation of written or spoken input from various sources.
We are in the midst of the AI revolution. A recent report by PwC and another by McKinsey both place AI as the biggest commercial opportunity for “companies and nations” over the coming decades. 30% of businesses are already conducting AI pilots, and nearly half are using AI capabilities within at least one standard business process — up from 20% in 2017.
Cloud-based service provisioning has entered the machine learning ecosystem, and the outcome is MLaaS. The long and the short of it is that machine learning-as-a-service is exactly what it sounds like — it's the delivery of accessible, hosted and subscription-based machine learning tools that are ‘ready-to-go’. But is MLaaS right for you? And is there more to understanding the nuance of MLaaS?