Five D’s of Analytic Model Deployment
Moving your models from the lab to the field for business impact
The Challenges: Business Adoption of Analytic Models In order to increase business adoption of analytic models, there is a great deal of work that must occur in addition to model development, and it extends well beyond the model development team. … Read More
Entertaining Machine Learning Failures
Proof that robots won't be taking over any time soon.
Unless you’ve been living under a rock, the question of “Humans vs Machines” is not new to you. Although the volume may have recently increased due to technological advances, the story has been told over and over again, in a … Read More
Should Data Scientists be threatened by AlphaGo Zero?
Google unveils new AI that can "create knowledge itself"
The discussion amongst leading scientists concerning the cultural and social implications for artificial intelligence (AI) is in the daily news, resulting in a public, intellectual debate over whether AI will be a harmful, helpful, or benign influence on society. Notably … Read More
Lessons Learned: Tying Operational Business Decisions to Model Scores
Data without decisions will not get you anywhere!
The typical output of a model, usually called a “score,” is essentially a ranking or estimation of the most likely business outcomes, such as the most likely customer behavior in response to a stimulus (or the absence of a stimulus). … Read More
Corios Tempo: operationalizing analytics via a model factory process
Reducing costs and increasing throughput of analytics assets
Predictive assets aren’t academic exercises The biggest challenge that confronts predictive modelers and data scientists is that it doesn’t matter how good any model is, if it isn’t used by other people in your organization to improve the way they … Read More