AI 102 | Microsoft Azure AI Engineer Training at Visualpath
                     Different Deployment 
Options for Azure AI 
Models
Exploring flexible and scalable AI deployment strategies with Azure 
for AI developers and IT professionals.
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Introduction
Versatile Deployment Flexible Inference Key Considerations
Azure AI offers diverse Models support real-time, batch, Scalability, cost, latency, and 
deployment options for various or edge inference needs. security are crucial factors.
workloads.
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Azure Machine Learning Endpoints
Managed Online Endpoints Batch Endpoints
• Fully managed for real-time inference. • Processes large, offline datasets in parallel.
• Supports autoscaling and zero-downtime updates. • Eliminates manual infrastructure management.
• Ideal for low-latency, high-throughput applications. • Perfect for periodic reporting or large-scale data transformation.
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Azure Kubernetes Service (AKS)
AKS is ideal for production-scale deployments, offering robust 
features for complex AI workloads. It provides high availability, 
advanced networking, and fine-grained autoscaling capabilities to 
handle fluctuating demand.
Enterprises choose AKS for scenarios demanding deep 
customization and precise control over the underlying 
infrastructure, from GPU utilization to custom network 
configurations.
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Azure Container Instances (ACI)
ACI is a lightweight, serverless container deployment option for 
rapid AI model execution. It's best suited for development, testing, 
or low-traffic scenarios where quick iteration is key.
Spin up AI models in seconds without the overhead of managing 
clusters or VMs. ACI provides a simple, on-demand solution for 
smaller, self-contained inference tasks.
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Edge Deployment (Azure IoT Edge)
Local Processing Reduced Latency
Run AI models directly on IoT edge devices. Minimizes prediction latency and cloud dependency.
Diverse Use Cases Offline Capabilities
Ideal for industrial IoT, manufacturing, and remote Enables continuous operation even without internet 
environments. access.
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Integration with Azure 
Functions & Logic Apps
Azure Functions Logic Apps
• Deploy AI models as • Visually design automated 
serverless functions. workflows.
• Trigger predictions on- • Integrate models with 
demand by events. business processes and 
• Pay only for execution time, systems.
highly cost-effective. • Connect to hundreds of 
• Ideal for sporadic, event- services without writing 
driven inference. code.
• Automate complex decision-
making based on AI insights.
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Azure Cognitive Services Custom 
Deployment
Extend Azure's prebuilt AI services with custom container support. 
Deploy services like Vision, NLP, and Speech to on-premises or 
hybrid environments.
This provides unprecedented flexibility for organizations with strict 
data privacy, compliance, or low-latency requirements, keeping 
sensitive data closer to its source.
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Summary & Best Practices
Align with Needs Control & Privacy
Choose deployment based on real-time vs. batch, Consider data privacy, compliance, and 
and scalability. infrastructure control needs.
Edge Computing Azure's Advantage
Utilize edge deployments for low-latency and Azure offers diverse options for every AI scenario, 
offline scenarios. ensuring flexibility and performance.
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For More Information About 
  AZURE AI
Address:- Flat no: 205, 2nd Floor,
   Nilagiri Block, Aditya Enclave,  Ameerpet, 
Hyderabad-16
   Ph. No: +91-998997107
  www.visualpath.in
    [email protected]
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Thank You
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