Leveraging business transformation with machine learning experiments

• The deployment of production-quality ML solutions, even for simple applications, requires significant software engineering effort. Often, companies do not fully understand the consequences and the business impact of ML-based systems, prior to the development of these systems. To minimize investment risks while evaluating the potential business impact of an ML system, companies can utilize continuous experimentation techniques. Based on action research, we report on the experience of developing and deploying a business-oriented ML-based dynamic pricing system in collaboration with a home shopping e-commerce company using a continuous experimentation (CE) approach. We identified a set of generic challenges in ML development that we present together with tactics and opportunities.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Keyword

Retail industry

Dynamic pricing

Machine learning

Business transformation

Continuous experimentation

Publication and Content Type

kon (subject category)

ref (subject category)