For Executives

The executive role in effective machine learning is critical. DataRobot University can help you learn how to drive success in machine learning, whether or not your team uses the DataRobot platform.

Three-Hour Seminar

Machine Learning and AI for Executives

Appropriate for business executives sponsoring, funding, or implementing machine learning initiatives.

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Gain insight into how to drive success in machine learning. Identify key points in the machine learning life cycle where executive oversight really matters. Learn effective methods to help your team deliver better predictive models, faster. You'll leave this seminar able to identify business challenges well suited for machine learning, with fully defined predictive analytics projects your team can implement now to improve operational results. Attendance at this seminar is by invitation. If you would like to be invited, please contact your DataRobot representative or email dru@datarobot.com .

1. Machine learning and you

  • The machine learning line-up
  • Definitions, buzzwords, and hype
  • Making big data have a big impact

2. Implementing machine learning

  • Organizational approaches
  • Enterprise machine learning
  • Machine learning-driven enterprises

3. Identifying opportunities

  • Problems you can solve
  • Evaluating and prioritizing
  • Effective execution
  • Nov $500

    15
    London, UK
Three-Hour Seminar

Ethical AI

Appropriate for anyone sponsoring, funding, or implementing machine learning initiatives.

Gain insight into how ethics can impact your AI initiatives. Identify ethical issues within the machine learning life cycle and spot potential risks before they occur. You'll leave this seminar able to identify ethical challenges related to machine learning projects, with a framework for developing your organization’s ethical AI policy. Attendance at this seminar is by invitation. If you would like to be invited, please contact your DataRobot representative or email dru@datarobot.com .

1. Ethics and risk

  • Ethical purpose
  • Making value judgements
  • Disclosure of AI usage

2. Identifying ethical issues

  • Defining fairness and bias
  • Model transparency and
    interpretability
  • Labor Displacement

3. Developing an ethics policy

  • Appropriate data sources
  • Making predictions vs.
    decisions
  • Reviews and appeal process

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