In January 2026, Microsoft and NVIDIA released the second iteration of the NVIDIA Dynamo Planner—a groundbreaking tool for optimizing large language model (LLM) inference on Azure Kubernetes Service (AKS). This collaboration addresses one of the most challenging aspects of production AI: efficiently scaling GPU resources to balance cost, latency, and throughput. This comprehensive guide explores […]
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Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring
Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.
Read more →Alternative Cloud AI Platforms: IBM watsonx, Oracle OCI, Databricks & Snowflake Deep Dive
Beyond AWS, Azure, and GCP—explore IBM watsonx, Oracle OCI, Databricks, and Snowflake AI platforms. Complete guide with architectures, code examples, and when to choose each platform.
Read more →DIY LLMOps: Building Your Own AI Platform with Kubernetes and Open Source
Build a production-grade LLMOps platform using open source tools. Complete guide with Kubernetes deployments, GitHub Actions CI/CD, vLLM model serving, and Langfuse observability.
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 →Enterprise GenAI: Taking AI Applications from Prototype to Production at Scale
Deploy GenAI at enterprise scale. Learn model routing, observability, security patterns, cost management, and what the future holds for AI in production.
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