Azure AI Engineering Deep Dive: Enterprise Production & Scale
Deploy and operate AI at enterprise scale with secure architecture, observability, governance, and cost optimization for production Azure AI workloads.
Moving AI solutions into production introduces new challenges in security, scalability, governance, and operational visibility. This advanced engineering deep dive teaches how enterprise teams deploy, secure, monitor, and optimize AI workloads on Azure at scale.
Participants explore real-world architecture patterns for operating AI systems in complex environments, including asynchronous processing, private networking, multi-model routing strategies, observability practices, and enterprise governance frameworks.
Hands-on labs focus on building production-ready AI workflows that integrate automation, monitoring, and security controls — helping teams design AI platforms that are resilient, compliant, and cost-efficient.
Key Skills Covered
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Scaling AI workloads with event-driven architectures
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Enterprise security patterns for AI systems
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Managed identities and private networking design
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Observability, telemetry, and usage monitoring
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Cost optimization and performance tuning
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Governance and human-in-the-loop workflows
Who Should Attend
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Senior cloud engineers
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AI platform engineers
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Enterprise architects
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DevOps and platform engineering teams
Suggested Prerequisites
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Experience with enterprise AI architectures (see: Azure AI Engineering Deep Dive: RAG & Intelligent Agents)
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Understanding of distributed systems and cloud networking concepts
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Experience with Azure infrastructure and deployment workflows