Hudson Buzby
Solutions Architect at JFrog
Hudson Buzby is a solution engineer with a strong focus on MLOps and LLMOps, 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.
All Sessions by Hudson Buzby
Solution Showcase: Trusting AI: Is it really possible
<span class="etn-schedule-location"> <span class="secfocus">All Levels</span> </span>Generative AI and machine learning systems are reshaping industries but also introducing new security risks. The reliance on vast data, rapid deployment cycles, and automated pipelines in MLOps has expanded the attack surface, exposing vulnerabilities to data poisoning, adversarial inputs, and pipeline exploitation. This session explores the unique security challenges of ML systems in the GenAI era and provides actionable strategies to safeguard them. Learn why traditional approaches fall short and how to fortify your ML lifecycle to stay ahead in an evolving threat landscape.
Securing AI/ML Development in the Age of Hugging Face
<span class="etn-schedule-location"> <span class="firstfocus">LLMOps & MLOps</span> <span class="secfocus">Intermediate</span> </span>Abstract Coming Soon!
Securing AI/ML Development in the Age of Hugging Face
<span class="etn-schedule-location"> <span class="firstfocus">LLMOps & MLOps</span> <span class="secfocus">Intermediate</span> </span>Abstract Coming Soon!
Solution Showcase: Trusting AI: Is it really possible
<span class="etn-schedule-location"> <span class="secfocus">All Levels</span> </span>Generative AI and machine learning systems are reshaping industries but also introducing new security risks. The reliance on vast data, rapid deployment cycles, and automated pipelines in MLOps has expanded the attack surface, exposing vulnerabilities to data poisoning, adversarial inputs, and pipeline exploitation. This session explores the unique security challenges of ML systems in the GenAI era and provides actionable strategies to safeguard them. Learn why traditional approaches fall short and how to fortify your ML lifecycle to stay ahead in an evolving threat landscape.