Advances in data science have enhanced organizations’ ability to accurately predict the future, oftentimes with a keen focus on prediction quality (e.g. accuracy of a forecast). While this is an important capability, the use of data science techniques to provide explainability should not be overlooked as an additional tool for data-driven decision-making.

An elasticity model explains the relationship of input factors to demand and provides a technique for modeling various outcome scenarios based on potential changes to inputs. In contemporary, hyper-competitive markets, understanding these variables and their relationship to overall consumer demand can provide a critical edge for those who get it right.
In this recorded webcast, Deb Lee and Alaistair Jones share how we have applied MLOps techniques to create flexible, real-time models that can be used to model elasticities across product and other attribute hierarchies. We’ll discuss the value of reliable Machine Learning CI/CD pipelines built upon a deep understanding of data and business operations, the role of Machine Learning and AI, explore how real-time model deployments can add value for scenario analysis, and common approaches to grow and nurture data solutions.

What we’ll cover:

Drawing from client experiences, we explore:

• Common architecture design patterns featuring data engineering, data science, and data visualization components.
• How real-time machine learning models can be deployed using MLOps best practices to empower advanced decision-making using scenario analysis
• Key business drivers and how we can ensure alignment to business operations when delivering decision-making systems.
• Our approach to using data and analytic technology and methodologies to help customers leverage market data for elasticity modeling and other initiatives.

Is it for you?

• Are you looking to start or recently started an initiative to model elasticities (e.g. price, demand)?
• Have you been exploring modern technologies and data science techniques to improve how you explain consumer behavior in a competitive market?
• Are you looking to prove or disprove hypotheses around why recent fluctuations of demand may have occurred in a market?
• Would you like to learn about the value of focusing on explainability and how Machine Learning at scale using MLOps can support decision making?