Navigating Open-Source Management with AI

This chapter explores the evolution of the JFrog platform and its incorporation of machine learning to enhance enterprise-level open-source management. It emphasizes the need for governance, automated features, and an 'AI catalog' to ensure compliance and security in an increasingly complex development landscape.

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Trust at Scale: Security and Governance for Open Source Models // MLOps Podcast #338 with Hudson Buzby, Solutions Architect at JFrog.

Appreciate JFrog for their support in bringing this blog to life.

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// Abstract

For better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation, we will discuss ways that enterprise-scale organizations are addressing these challenges to safely and securely build these development environments.

// Bio

Hudson Buzby is a solution engineer with an emphasis on MLOps, LLMOps, Big Data, and Distributed Systems, leveraging his expertise to help organizations optimize their machine learning operations and large language model deployments. His role involves providing technical solutions and guidance to enhance the efficiency and effectiveness of AI-driven projects.

// Related Links

https://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg

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Timestamps:

[00:00] Value of Centralized Gateway

[00:35] Point Break vs Big Lebowski