Infrastructure as Code for AI: Terraform Patterns for LLM Deployments Expert Guide to Managing AI Infrastructure with Terraform I’ve managed AI infrastructure across AWS, Azure, and GCP using Terraform. Infrastructure as Code isn’t just about automation—it’s about reproducibility, version control, and managing complex AI deployments consistently. When you’re deploying LLM services, vector databases, and GPU […]
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Cloud LLMOps: Mastering AWS Bedrock, Azure OpenAI, and Google Vertex AI
Deep dive into cloud LLMOps platforms. Compare AWS Bedrock, Azure OpenAI Service, and Google Vertex AI with practical implementations, RAG patterns, and enterprise considerations.
Read more →Beyond Chatbots: Why Agentic AI Is the Most Transformative Technology Shift Since the Cloud
We’ve reached an inflection point in artificial intelligence that most organizations haven’t fully grasped yet. While the world obsesses over chatbots and prompt engineering, a more profound shift is quietly reshaping how software systems operate. Agentic AI—autonomous systems capable of reasoning, planning, and executing multi-step tasks without constant human intervention—represents the most significant architectural transformation […]
Read more →Cloud-Native AI Architecture: Patterns for Scalable LLM Applications
Cloud-Native AI Architecture: Patterns for Scalable LLM Applications Expert Guide to Building Scalable, Resilient AI Applications in the Cloud I’ve architected AI systems that handle millions of requests per day, scale from zero to thousands of concurrent users, and maintain 99.99% uptime. Cloud-native architecture isn’t just about deploying to the cloud—it’s about designing systems that […]
Read more →MLOps vs LLMOps: A Complete Guide to Operationalizing AI at Enterprise Scale
Understand the critical differences between MLOps and LLMOps. Learn prompt management, evaluation pipelines, cost tracking, and CI/CD patterns for LLM applications in production.
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
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