AWS Bedrock: Building Enterprise Generative AI Applications on AWS

AWS re:Invent 2024 brought significant updates to Amazon Bedrock, and after spending the past month integrating these capabilities into production systems, I want to share what actually matters for enterprise adoption. Having built generative AI applications across multiple cloud platforms over the past two decades, Bedrock represents a meaningful shift in how we can deploy […]

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Model Context Protocol (MCP): Building AI-Tool Integrations That Scale

Introduction: The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI assistants to securely connect with external data sources and tools. Think of MCP as a universal adapter that lets AI models interact with your files, databases, APIs, and services through a standardized interface. Instead of building custom integrations for […]

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Data Lakehouse Architecture: Bridging Data Lakes and Data Warehouses

After two decades of building data platforms, I’ve witnessed the pendulum swing between data lakes and data warehouses multiple times. Organizations would invest heavily in one approach, hit its limitations, then pivot to the other. The data lakehouse architecture represents something different—a genuine synthesis that addresses the fundamental trade-offs that forced us to choose between […]

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Azure OpenAI Service with Python: Building Enterprise AI Applications

After spending two decades building enterprise applications, I’ve watched countless “revolutionary” technologies come and go. But Azure OpenAI Service represents something genuinely different—a managed platform that brings the power of GPT-4 and other foundation models into the enterprise with the security, compliance, and operational controls that production systems demand. Here’s what I’ve learned from integrating […]

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