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OUR SPEAKERS
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Nnenna Ndukwe is a Developer Advocate and a Software Engineer, enthusiastic about DevOps and AI. She's an experienced software engineer in medtech, fintech, and media tech. She studied Computer Science at Boston University and is a Resilient Coders alum. A proud member of Women Defining AI, CNCF, Open Source Security Foundation, Cyber Women of Boston, and more. She's a Google Women Techmakers Scholar, NSBE Engineering award winner, and an international speaker. She shares her journey with the world via content creation, volunteering, and speaking at tech events.
Nnenna Ndukwe
Developer Advocate
English
Languages:
Location:
Boston, United States
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
MY TALKS
5 Security Best Practices for Production Ready Containers
Software Engineering, Backend, Security / Privacy
Most application developers would rather write great code and ship cool new features than patch security issues. This talk provides highlights of our investigation and simple steps developers can take to address security issues in their containers BEFORE they get to production.
No developer wants to be part of the morning news due to a security breach, but most application developers would rather write great code and ship cool new features than patch security issues. Thankfully, with simple best practices, developers can significantly reduce the attack surface of their containers before shipping them to production. At Slim.AI, we analyzed hundreds of container images accounting for billions of pulls annually to better understand the risks facing developers today. This talk provides highlights of our investigation and simple steps developers can take to address security issues in their containers BEFORE they get to production.
Democratizing AI: Building Resilient and Secure Open-Source LLMs for Digital Sovereignty
Software Engineering, Data / AI / ML, DevOps
This presentation explores the critical role of open-source LLMs in achieving digital sovereignty. We will delve into the technical challenges and opportunities in building secure and resilient open-source LLMs, focusing on practical strategies for data privacy, model security, and community governance. We will examine case studies showcasing successful community-driven initiatives and discuss best practices for fostering collaboration and knowledge sharing within the open-source ecosystem.
From DevOps to MLOps: Bridging the Gap Between Software Engineering and Machine Learning
Software Engineering, Data / AI / ML, QA / Automation, DevOps
Both DevOps and MLOps aim to streamline the development and deployment lifecycle through automation, CI/CD, and close collaboration between teams. But there are key differences in the purposes and applications of DevOps and MLOps. This talk demonstrates how your existing DevOps expertise creates a strong foundation for understanding and implementing MLOps practices. We'll explore how familiar concepts like CI/CD, monitoring, and automated testing map to ML workflows, while highlighting the key differences that make MLOps unique.
Through practical examples, we'll show how software engineers can apply their current skills to ML systems by extending DevOps practices to handle model artifacts, training pipelines, and feature engineering. You'll learn where your existing tools and practices fit in, what new tools you'll need, and how to identify when MLOps practices are necessary for your projects.
5 Security Best Practices for Production Ready Containers
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Democratizing AI: Building Resilient and Secure Open-Source LLMs for Digital Sovereignty
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From DevOps to MLOps: Bridging the Gap Between Software Engineering and Machine Learning
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