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ONNX Deployment: Cross-Platform AI Model Deployment
ONNX enables train-once-deploy-anywhere for AI models across diverse hardware.
Deployment
17 articles
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Phi-3 Local Deployment: Running Microsoft's SLM On-Device
Phi-3 enables powerful AI capabilities entirely on-device with no cloud dependency.
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ONNX Runtime: Cross-Platform AI Deployment
ONNX Runtime is the unsung hero of AI deployment. Today I'm exploring how to use it for consistent AI inference across platforms.
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Deploying GPT-4o on Azure OpenAI Service
1. Navigate to your Azure OpenAI resource 2. Go to Model deployments Deploy model 3. Select from the model list 4. Configure deployment settings 5. Deploy…
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Databricks Model Serving Updates: April 2024
Databricks Model Serving continues to evolve with new features for deploying and scaling ML models. This guide covers the latest updates and best practices.
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Fabric CI/CD: Building Deployment Pipelines
Implementing CI/CD for Microsoft Fabric ensures reliable, repeatable deployments of analytics artifacts. This guide covers building end-to-end deployment…
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Gradual Rollout Strategies for AI Features
Deploying AI features to production requires careful risk management. Gradual rollouts help identify issues early while minimizing blast radius.
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Feature Flags for AI: Controlling AI Rollouts
Feature flags provide fine-grained control over AI features, enabling safe deployments, quick rollbacks, and targeted releases. This guide covers…
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Custom Model Deployment on Azure: From Training to Production
Deploying custom AI models to production requires careful consideration of scalability, reliability, and cost. This guide covers the complete journey from…
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Model Optimization Techniques for Production Deployment
Tomorrow we'll explore quantization basics in more detail. PyTorch Quantization Flash Attention torch.compile
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Model Deployment Patterns for Azure OpenAI
These deployment patterns enable safe, controlled rollouts of LLM application changes. Start with gateway and blue-green patterns, then add canary and A/B…
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Deployment Gates in Azure DevOps
Gates automate quality assurance in your deployment pipelines.
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Azure DevOps Environments and Deployments
Environments provide governance and visibility for deployments across your infrastructure.
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Blue-Green Deployment for ML Models
Blue-green deployment provides a safe, zero-downtime approach to updating ML models in production.
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Azure ML Managed Online Endpoints for Model Deployment
Managed online endpoints simplify model deployment while providing enterprise-grade reliability and scalability.
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ALM Toolkit for Power BI: Schema Comparison and Deployment
ALM Toolkit is essential for Power BI DevOps: Visual schema comparison Controlled deployments Integration with CI/CD Environment management ALM Toolkit on…
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Power BI Deployment Pipelines: CI/CD for Analytics
Deployment Pipelines bring enterprise ALM to Power BI: Controlled content promotion Environment specific configurations Audit trail of deployments…