Introduction: BigQuery stands as Google Cloud’s crown jewel—a serverless, petabyte-scale data warehouse that has fundamentally changed how enterprises approach analytics. This comprehensive guide explores BigQuery’s enterprise capabilities, from columnar storage and slot-based execution to advanced features like BigQuery ML, BI Engine, and real-time streaming. After architecting data platforms across all major cloud providers, I’ve found […]
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ETL for Vector Embeddings: Preparing Data for RAG
Preparing data for RAG requires specialized ETL pipelines. After building pipelines for 50+ RAG systems, I’ve learned what works. Here’s the complete guide to ETL for vector embeddings.
Read more →Feature Engineering at Scale: Building Production Feature Stores and Real-Time Serving Pipelines
Introduction: Feature engineering remains the most impactful activity in machine learning, often determining model success more than algorithm selection. This comprehensive guide explores production feature engineering patterns, from feature stores and versioning to automated feature generation and real-time feature serving. After building feature platforms across multiple organizations, I’ve learned that success depends on treating features […]
Read more →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.
Read more →Microsoft Azure AI Foundry: The Complete Guide to Enterprise AI Development
Introduction: Microsoft Azure AI Foundry (formerly Azure AI Studio) represents Microsoft’s unified platform for building, evaluating, and deploying generative AI applications. Announced at Microsoft Ignite 2024, AI Foundry consolidates Azure’s AI capabilities into a single, cohesive experience that spans model selection, prompt engineering, evaluation, fine-tuning, and production deployment. With access to Azure OpenAI models, Meta […]
Read more →Airflow on Kubernetes in Production: Architecture, Deployment, and Cost Optimization
Production-tested patterns for running Apache Airflow on Kubernetes with the KubernetesExecutor. Covers architecture, deployment, auto-scaling, cost optimization, and real-world case studies achieving 40-60% cost savings.
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