Cesar Mierzejek | Data Science Leadership Skills

06/17/20 • 18 min

The Data Science Podcast | IBM Developer Podcasts

Kinga and Cesar discuss his career at IBM and the skills he needed to learn to be a top data scientist. The key topics explored include: Learning to communicate the value of your work and why it matters, being succinct and focusing on what your stakeholder needs; although unintuitive, learning about product management as it has helped him focus on the customers for the model, and finally becoming a life long learner.

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IBM Data Science Community

Links mentioned in this episode:

IBM Data Science Community

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Nathalie Baracaldo | Federated Learning

IBM Research recently introduced their perspective on a machine learning paradigm called Federated Learning in which multiple parties can all participate in training a single model with a shared goal. You can use data that is distributed between competitors, or even data distributed in one company across multiple geographies. They can participate in this so securely without sharing their raw data, and consequently get models that are much more generalizable than they would otherwise be able to achieve on their own.

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Mike Tamir | Reinforcement Learning

Reinforcement learning is a paradigm of artificial intelligence in which we model both actor and environment, and let the actor explore that environmental space.

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