November 13, 2018
Managing Director, Analytic Strategy
Despite all the promise of data science and machine learning, executives and data scientists are still frustrated by the difficulty of deploying models extensively and at scale. Technology solutions can help organizations get there but much of the responsibility falls on the data science team to engage with the business to make sure their models have the right impact at the right time.
This presentation will talk through common practices and pitfalls for getting models from prediction to business decision during model development and deployment. You will learn a blend of soft skills and technical skills, drawing on real-life examples that show how to drive better value and adoption of data science and machine learning. The first focus is on communication and influencing skills to align data science projects with decision makers who greenlight the deployment of models. The second focus is on the rapidly expanding field of explainable machine learning that seeks to bridge the gap between highly complex models and stakeholders who are hesitant to trust “black-box” decision support systems.
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