Building AI Models for the Enterprise

Comments on Chief Data Scientist Survey

Wing Venture Capital ( “Wing” ) recently published the results of a data scientist survey they recently completed. Wing surveyed 88 data scientists working with public & venture-backed companies ranging in market cap from under $1 billion to many over $50 billion. We were impressed with one table in particular that outlined the challenges in building models for enterprise deployment:

Reviewing the results left us wondering if the data scientists were not reading from our CVX platform specifications in responding to their most significant challenges in modeling ??. 

Our CVX v2.5 AI platform focuses on maximizing key performance indicators for customer experience ( CX ), such as measuring commitment to a brand, up-selling, churn reduction, etc.

Our Cerebri AI CVX platform was designed & built to operate at enterprise-scale processing, both very large datasets and dozens of CX models. Using our decades of enterprise computing experience, we created not only a seamless AI platform, but we also help drive AI-based scores and metrics deeper into large organizations where they can really drive value.

Our CVX platform makes data science more productive by making modeling faster and more reliable. It also features APIs & end-user applications that enable enterprise users to more easily meet their key performance objectives by more systematic use of AI-derived CX measures and scores. 

The three core challenges in driving AI into the enterprise at scale are highlighted in the survey:

• data: easy intaking & handling of both streaming & batch data, labeling data for multiple models using the same customer journeys, making decisions at the speed of digital, real-time monitoring of data quality, data engineering at its best.
• models: explainability of model features for both raw features & engineered features with total transparency for both data scientists and end-users, real-time quality management of model performance in pre- & post-deployment phases, easy deployment of models especially in quicker re-training scenarios for critical models, effective design, development & cataloging of engineered features optimizing model performance, understanding and execution of sophisticated model validation especially in regulatory environments.
• users: not only data scientists but also end-users of models who must have high-quality models they can rely on, tracking of results, testing, and of course, ease-of-use, which is always front and center when deploying the results of complex technology like AI.

If you are worried your competitors are getting ahead and that the onslaught of digital everything is changing your business – give us a call or email – we would love to speak to you.

Jean Belanger, Co-founder & CEO - Cerebri AI inc. 

M: +1 (512) 909 3431 E: [email protected] ]