Kubernetes 1.35: In-Place Pod Resource Updates and AI Model Image Volumes

Kubernetes 1.35, released in January 2026 and now supported on Amazon EKS and EKS Distro, marks a significant milestone in container orchestration—particularly for AI/ML workloads. This release introduces In-Place Pod Resource Updates, allowing you to resize CPU and memory without restarting pods, and Image Volumes, a game-changer for delivering large AI models using OCI container […]

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Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python

After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]

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Production-Ready Agents: Observability, Security & Deployment – Part 8

Deploy AI agents to production with enterprise-grade observability, security, and resilience. Complete guide to OpenTelemetry, content safety, and Azure deployment.

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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.

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Diving Deeper into Docker: Exploring Dockerfiles, Commands, and OCI Specifications

Docker is a popular platform for developing, packaging, and deploying applications. In the previous blog, we provided an introduction to Docker and containers, including their benefits and architecture. In this article, we’ll dive deeper into Docker, exploring Dockerfiles, Docker commands, and OCI specifications. Dockerfiles Dockerfiles are text files that contain instructions for building Docker images. […]

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