🤖 AI Infrastructure for Platform Engineering

AI Infrastructure refers to the foundational systems, tools, and platforms required to develop, deploy, and scale artificial intelligence (AI) and machine learning (ML) workloads. For platform engineering teams, building robust AI infrastructure means enabling data scientists, ML engineers, and developers to efficiently train, serve, and manage AI models—while ensuring scalability, security, and operational excellence.

🏗️ What Does AI Infrastructure Include?

Compute Resources: High-performance CPUs, GPUs, and TPUs for training and inference.
Storage: Scalable, high-throughput storage for datasets, models, and logs.
Networking: Fast, reliable networking for distributed training and data movement.
Orchestration: Tools like Kubernetes for managing containerized AI workloads.
Model Serving: Systems for deploying and scaling AI models in production (e.g., KServe, Seldon Core).
Monitoring & Observability: Tracking model performance, resource usage, and drift.
Security & Compliance: Managing access, data privacy, and auditability.

🚀 AI Infrastructure Submodules

Explore these key submodules to learn how platform engineering teams can implement and scale AI infrastructure:

Running LLMs Inside Kubernetes
Coming soon: Guidance on how to deploy and manage large language models (LLMs) such as GPT or Llama within Kubernetes clusters for scalable, secure, and integrated AI inference.
Running LLMs Locally with Azure Foundry Local
Coming soon: How to run large language models (LLMs) on your own infrastructure using Azure Foundry Local for performance, privacy, customization, and cost advantages. Learn more here.
Model Context Protocol (MCP)
An open standard for describing, sharing, and managing the context in which AI models operate—enabling interoperability, reproducibility, and integration across platforms. Learn more at the official MCP site.

🌐 Why AI Infrastructure Matters for Platform Engineering

Scalability: Meet growing AI/ML workload demands.
Efficiency: Automate deployment, scaling, and monitoring of models.
Security: Enforce policies and compliance for sensitive data and models.
Innovation: Enable rapid experimentation and faster time-to-value for AI initiatives.

📚 Further Reading

AI infrastructure is a critical enabler for modern platform engineering, empowering teams to deliver intelligent applications at scale—whether running on Kubernetes, leveraging managed cloud platforms, or combining both approaches.