**Private data, Data Science friendly**
Data Scientists are always eager to get their hands on more data, in particular, if that data has any value that can be extracted. Nevertheless, in real-world situations, data does not exist in the abundance that we thought existed, in other situations, the data might exist, but not possible to share it with different entities due to privacy concerns, which makes the work of data scientists not only hard, but sometimes even impossible.
// Abstract:
In the last episode of this series, we've decided to bring not one, but two guests to tells us how Synthetic data can unlock the use of data for Data Science teams whenever privacy concerns are a reality.  Jean-François Rajotte, Researcher and Resident data Scientist at the University of Columbia and Sumit Mukherjee, Senior Applied Scientist at Microsoft's AI for Good, bring us into more detail their expertise not only, in Synthetic data generation, but in it's mind blowing combination with Federated Learning to take the healthcare sector into the next level of AI adoption.

//Other links to check on Jean-François Rajotte:
https://venturebeat.com/2021/01/20/microsofts-felicia-taps-ai-to-enable-health-providers-to-share-data-anonymously/
https://dsi.ubc.ca/
https://leap-project.github.io/

//Other links to check on Sumit Mukherjee:
www.sumitmukherjee.com (Sumit research)
https://arxiv.org/abs/2101.07235
https://arxiv.org/pdf/2009.05683.pdf
https://github.com/microsoft/privGAN (PrivGan)

//Final thoughts
Feel free to drop some questions into our slack channel (https://go.mlops.community/slack) 
Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ
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Connect with Sumit on LinkedIn: https://www.linkedin.com/in/sumitmukherjee2/