Build a production-ready Research Assistant AI agent using Python. Complete tutorial covering async patterns, @ai_function decorators, multi-turn conversations, and best practices.
Read more →Month: October 2025
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 →The Modern Data Engineer’s Toolkit: Why Python Became the Lingua Franca of Data Pipelines
After 20 years building data pipelines across multiple languages—Java, Scala, Go, Python—I’ve watched Python evolve from a scripting language to the undisputed standard for data engineering. This article explores why Python became the lingua franca of data pipelines and shares production patterns for building enterprise-grade systems. 1. The Evolution: From Java to Python In 2005, […]
Read more →Disaster Recovery for AI Systems: Multi-Region Deployment Strategies
Disaster Recovery for AI Systems: Multi-Region Deployment Strategies Expert Guide to Building Resilient AI Systems Across Multiple Regions I’ve designed disaster recovery strategies for AI systems that handle millions of requests per day. When a region goes down, your AI application shouldn’t. Multi-region deployment isn’t just about redundancy—it’s about maintaining service availability, data consistency, and […]
Read more →Building Your First AI Agent with Microsoft Agent Framework (.NET) – Part 2
Build a production-ready Customer Support AI agent using C# and .NET 8. Complete tutorial covering project setup, tools, multi-turn conversations, middleware, and error handling.
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.
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