Converseon.AI Launches First Comprehensive Library of Prebuilt AI-Powered Language Models to Rapidly Accelerate the Adoption, Value and Use of Social Listening Data

 Rob Key

Rob Key

October 10, 2018 - Jane Quigley, Chief Client Officer at Converseon, writes: Converseon announced the release of the industry’s first comprehensive library of prebuilt machine learning models designed to drive enhanced value and use of social listening data. These models, built via the firm’s award-winning machine learning-as-a-service platform, Conversus.AI, enables brands to easily and cost-effectively choose and subscribe to their preferred prebuilt, industry-centric models and deploy them immediately either directly or in conjunction with a growing array of leading social listening, management and business intelligence platforms.

Each model has been rigorously designed and tested for highest impact and exceed a minimum threshold of .80 F1 performance score (precision and recall). By making these models available for immediate subscription and deployment, brands can rapidly accelerate the use of social data and insights in their organization, and avoid the significant cost and time trying to build models from scratch.

Forrester Research, in its Tech Radar 2017 Report, forecasted the emergence of prebuilt machine models and its benefits by writing, “While many open source machine learning platforms are available, the cost to successfully implement them and produce useful models can range into the hundreds of thousands or even millions of dollars due to the need to train them on large, clean data sets and the time needed to experiment with several different models before deploying into production. Using prebuilt models from cloud-based platforms can be much more cost-effective…”

“Machine learning is powering a new generation of social data use and value by classifying data like humans do even when specific keywords are not present,” said Rob Key [pictured], CEO of Converseon. “It allows companies to move far beyond simple sentiment and emotion to expand the use of this data in critical areas.” More...