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Enterprise Security and Governance MLOps // Diego Oppenheimer // MLOps Coffee Sessions #45
Description
Coffee Sessions #45 with Diego Oppenheimer of Algorithmia, Enterprise Security and Governance MLOps.
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// Abstract
MLOps in the enterprise is difficult due to security and compliance. In this MLOps Coffee Session, the CEO of Algorithmia, Diego, talks to us about how we can better approach MLOps within the enterprise. This is an introduction to essential principles of security in MLOps and why it is crucial to be aware of security best practices as an ML professional.
// Bio
Diego Oppenheimer is co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products, including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
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Connect with Diego on LinkedIn: https://www.linkedin.com/in/diego/
Timestamps:
[00:00] Thank you, Diego and Algorithmia, for sponsoring this session!
[01:04] Introduction to Diego Oppenheimer
[02:55] Security
[04:42] "The level of scrutiny for apps and development and that of the operational software is much higher."
[07:40] "We take the Ops part of MLOps very, very seriously, and it's really about the operational side of the equation."
[09:22] MLSecOps
[11:42] "The code doesn't change, but things change cause the data changed."
[15:23] Maturity of security
[18:45] "To a certain degree, we have general parameters of software DevOps in software engineering and DevOps, and we're adapting it to this new world of ML."
[19:03] Development workflow
[20:58] "In the ideal world, you're just sitting in your data science platform, your auto ML platform, whatever it is that you're working with, you can push a model."
[22:50] Security, responsibility, and authentication
[23:38] "What you don't want to learn is how to do automation every single time there's a new use case. That's just not a good use of your time." [24:30] Hurdles needed to be cleared
[24:47] "I would argue that there's no such thing as Bulletproof in software. That doesn't exist. It never has and never will."
[26:25] Machine Learning security risks
1. Operational risk
2. Brand risk
3. Strategic risk
[28:23] Machine Learning security risk standards
[31:11] "There's a world where you can reverse engineer a model by