Masking, obfuscating, stripping, shuffling.
All the above techniques try to do one simple thing: keeping the data private while sharing it with third parties. Unfortunately, they are not the silver bullet to confidentiality.
All the players in the synthetic data space rely on simplistic techniques that are not secure, might not be compliant and risky for production.
At pryml we do things differently.
State of Artificial Intelligence 2022 (Ep. 196)
Improving your AI by finding issues within data pockets (Ep. 195)
Fake data that looks, feels, and behaves like production.(Ep.194)
Batteries and AI in Automotive (Ep. 193)
Collect data at the edge [RB] (Ep. 192)
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
What is spatial data science? With Matt Forest from Carto (Ep. 190)
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189)
History of data science [RB] (Ep. 188)
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187)
Embedded Machine Learning: Part 5 - Machine Learning Compiler Optimization (Ep. 186)
Embedded Machine Learning: Part 4 - Machine Learning Compilers (Ep. 185)
Embedded Machine Learning: Part 3 - Network Quantization (Ep. 184)
Embedded Machine Learning: Part 2 (Ep. 183)
Embedded Machine Learning: Part 1 (Ep.182)
History of Data Science (Ep. 181)
Capturing Data at the Edge (Ep. 180)
[RB] Composable Artificial Intelligence (Ep. 179)
What is a data mesh and why it is relevant (Ep. 178)
Environmentally friendly AI (Ep. 177)
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