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Beginner | Intermediate | Advance
DevOps for AI Program Overview
The DevOps for AI program focuses on equipping individuals with the skills required to manage and optimize the infrastructure, SRE, and DevOps workflows for modern Large Language Model (LLM) applications. Participants will learn how to deploy and scale AI applications using NVIDIA-powered infrastructure, including GPU management for model training and inferencing. The curriculum covers end-to-end workflows, from setting up cloud environments and provisioning resources to automating model deployment pipelines. Additionally, students will explore monitoring and observability for AI models, ensuring high availability, performance, and security across all stages of the model lifecycle. This hands-on program prepares professionals to efficiently operate, scale, and maintain cutting-edge AI systems in production environments.
Topics Covered
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Introduction to Platform engineering and DevOps
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Changing landscape of DevOps due to AI
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Developer first mindset for long term success
Hands on Labs
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Deploy a Simple Web Application
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Simulate Node Failure
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Update a Deployment
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Explore the Control Plane
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Break and Fix
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DevOps Engineers looking to specialize in AI infrastructure and LLM applications. 🚀
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Machine Learning Engineers who want to integrate DevOps practices for efficient model deployment and scaling. 🤖
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Site Reliability Engineers (SREs) interested in ensuring the reliability and performance of AI systems using GPU infrastructure. 🛠️
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Cloud Engineers eager to learn how to manage and optimize cloud environments for AI workloads on platforms like NVIDIA. 🌐