Doing ML with Personal Information

MLOps.community · Intermediate ·📐 ML Fundamentals ·6y ago
Skills: ML Pipelines80%

Key Takeaways

The video discusses handling personal identifiable information (PII) in machine learning, with a focus on HIPAA compliance and secure data storage, highlighting the importance of data governance and anonymization in ML training.

Full Transcript

do you have use cases that involve personal identifiable information if you do how do you handle that yeah great question Alessandra we do a lot of we are HIPAA compliant because many hospitals or health care professionals use Survey Monkey we also integrate with a lot of enterprises that provide us PII info and so a PII data and so we need to be able to work with that and so the way that works is our real time feature store is set up in a secure manner that only specific services can access it and it does contain the PII data since sometimes we do to use PII data during inference time and that's known to our users it's an opt-in thing where users have to opt into it however for training no PII data is available we can't make that accessible for training ever so you cannot train on any PII data it can be anonymized in certain use cases depending upon these case and we have an entire governance process set up that I'm not responsible for but we made sure to abide by it at each point to make sure that we are working there effectively and I think and I think the the PII data that we use in the real time cases are also like we do have the ability to be able to go back in and delete it if needed and the various other processes that have instead of been set up for to abide by it like the the data governance practices that we need to fulfill as as a server monkey org yeah that makes sense

Original Description

MLOps Community Meetup #4 In the 4th online meetup for our MLOps.community We spoke with Shubhi Jain, Machine Learning Engineer, and an all-around great guy! In this clip, he talks about how he does Machine learning with personal information (PII) This is an excerpt taken from the longer conversation that can be found here: https://youtu.be/oq1g4s2dUHE Every organization is leveraging machine learning (ML) to provide increasing value to their customers and understand their business. You may have created models too. But, how do you scale this process now? In this case study, we looked at how to pinpoint inefficiencies in your ML data flow, how SurveyMonkey tackled this, and how to make your data more usable to accelerate ML model development. Shubhi Jain is a machine learning engineer at SurveyMonkey where he develops and implements machine learning systems for its products and teams. Occasionally, he’ll create YouTube videos about Machine Learning in collaboration with Springboard, an e-learning platform. He’s always excited to bring his expertise and passion for Data and AI systems to the rest of the industry. In his free time, Shubhi likes hiking with his dog and accelerating his hearing loss at live music shows. This was a virtual fireside chat between Shubhi Jain, Demetrios Brinkmann and the MLOps community. Relevant links can be found below. Join our MLOps slack community: https://bit.ly/3aOTwgR Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shubhi Jain on Linkedin: https://www.linkedin.com/in/shubhankarjain/
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This video teaches how to handle personal identifiable information in machine learning, ensuring HIPAA compliance and secure data storage, and highlights the importance of data governance and anonymization in ML training. It provides insights into real-time feature stores and opt-in processes for using PII data. By watching this video, viewers can learn how to build secure ML pipelines and implement data governance practices.

Key Takeaways
  1. Set up a secure real-time feature store
  2. Implement data governance practices
  3. Anonymize PII data for ML training
  4. Obtain user opt-in for using PII data
  5. Ensure HIPAA compliance
  6. Delete PII data when needed
💡 PII data can be used in real-time cases with user opt-in, but it must be anonymized for ML training and deleted when necessary, with a robust data governance process in place.

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