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Using ML to effectively target the right customer at the right time with the right information can make a big difference in how effective your campaign is. This short white paper discusses some of the approaches and demonstrates how one company achieved a 6x uplift in conversions by apply ML to customer targeting.
For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.
Healthcare Information Technology (HIT) is an indispensable part of managing and delivering healthcare services but patient data handling is highly regulated, presenting a challenge to practitioners. Learn how Starschema HealthLake can ensure compliance with the U.S. Health Insurance Portability and Accountability Act (HIPAA) while streamlining analytics.
Forward leaning companies are harnessing the power of the cloud to consolidate data silos into data lakes. Amazon Web Services has multiple services that can be used individually or in collaboration to kick-start the building of a data lake for any organization.
From recommender systems on streaming services to personal assistants like Siri or Alexa, ML is nearly everywhere today. This white paper demystifies machine learning for non-technical business stakeholders to better collaborate in, and derive more value from, data science initiatives.
Opening the Black Box - Learn how to think about AI and thrive in data-driven cultures of today and tomorrow
Machine Learning models can be so complex that they seem like a magical black box with inputs and outputs, but little understanding of how the outputs are derived. In this white paper,
Eszter Windhager-Pokol, Starschema head of data science, explains the concept of interpretability and several methods to address the problems posed by black box models.
The Standard Approach to Inference and Learning (SAIL) is a fast, efficient and scalable way to facilitate rapid, efficient and agile machine learning as well as inference.
Data scientists and manufacturing specialists can examine production lines to determine what data can be used to predict future failures and how to best collect it. A recent (2018) report on the manufacturing industry saw 31% of manufacturing CEOs expecting artificial intelligence and machine learning to contribute to a reduction in operating costs. Learn how predictive maintenance helps make this reality.