Edge Native: Bringing the Cloud-Native Development and Operations Experience to the Edge
Discover how to adapt cloud-native principles for edge computing, addressing challenges of distributed locations, AI deployment, and operational simplicity through practical demonstrations and solutions.
Decentralized Federated Machine Learning: Empowering Edge Devices with Kubernetes
Discover how to implement Federated Machine Learning with Kubernetes for privacy-preserving model training across edge devices, enabling efficient image classification and scaled computations.
Training and Serving LLMs on Kubernetes: A Beginner's Guide
Master the fundamentals of deploying and managing Large Language Models on Kubernetes, covering essential concepts from basic architecture to practical implementation strategies for training and serving.
Applying Open Source Methods to Building and Training Large Language Models
Discover how to contribute domain knowledge to open source AI models through innovative workflows, enabling incremental improvements without full model retraining or forking. Learn practical approaches to collaborative AI development.
Build Your Own CoPilot Using Azure OpenAI and Semantic Kernel
Discover how to create custom AI CoPilots using Azure OpenAI and Semantic Kernel, from understanding model training fundamentals to implementing personalized solutions for internal applications.
What Can Storage Do for AI? - Optimizing NVMe Storage for Large Language Models
Explore how NVMe flash storage enables AI model deployment at scale, from data center training to edge device inference, with focus on optimization techniques and performance improvements.
Open-Source and Science in the Era of Foundation Models
Explore the critical role of open-source models in AI development, focusing on API access, open weights, and community-driven innovation for advancing foundation model research.
On Spurious Associations and Large Language Model Alignment
Delve into the complex relationship between LLM alignment goals and their unintended effects, exploring how targeted improvements may create unexpected output changes.