End-to-End Multimodal LLMOps Project - Azure Deployment with Observability and Orchestration Engine

Learn to build a comprehensive multimodal LLMOps project that creates an automated Video Compliance QA Pipeline using Azure services and advanced orchestration techniques. Develop expertise in establishing a RAG (Retrieval-Augmented Generation) architecture that audits video content against regulatory standards through LangGraph orchestration. Master the integration of Azure Video Indexer for multimodal data ingestion including transcripts and OCR processing, while implementing Azure AI Search to retrieve relevant compliance rules using Azure OpenAI Embeddings. Build a sophisticated reasoning engine powered by Azure OpenAI's GPT-4o model that synthesizes multimodal data to detect compliance violations with deterministic accuracy. Implement production-grade observability through LangSmith for granular LLM workflow tracing and optimization, combined with Azure Application Insights for comprehensive telemetry, logging, and real-time performance monitoring. Transform unstructured video content into structured, actionable JSON compliance reports while maintaining full-stack observability throughout the entire pipeline. Gain hands-on experience with end-to-end deployment strategies, monitoring systems, and orchestration engines essential for enterprise-level multimodal AI applications.