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.

Links mentioned in this episode:

• IBM Data Science Community

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

Links Referenced in the Episode

• Reinforcement Learning: An Introduction
• IBM Data Science Community

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.

Links related to this episode:

• Nathalie Baracaldo, IBM Research - AI Security & Privacy
• Private federated learning - Learn together without sharing data
• Federated Learning Part 2

IBM's Data Science and AI Technical Marketing Team is launching a new series as part of the IBM Developer Podcast. In this short trailer, Will Roberts introduces himself and explains why his team is launching the Data Scientist Series and what they hope to achieve with it.

Links mentioned in this episode:

IBM Data Science Community

Yao Yang is a journalist turned machine learning researcher/ developer. She leads the R&D at Accenture Tech Labs to create a new platform that checks bias in data and mediate the biases in models to present a fair outcome for use, including processing and verifying data from various sources, recognizing opportunities and developing techniques for programmatically monitoring and enhancing trust through model development and execution.