Description
Take Your ML Models from Notebook to Production
The AI/ML Cloud Deployment Playbook bridges the gap between data science experimentation and production ML operations. Most organizations struggle not with building models but with deploying, monitoring, and maintaining them reliably in the cloud. This playbook provides proven deployment patterns for the three major cloud ML platforms, ensuring your models deliver value in production, not just in notebooks.
What’s Included
- AWS SageMaker deployment guide: real-time endpoints, batch transform, and serverless inference
- Google Vertex AI pipeline setup with custom training jobs, model registry, and endpoint management
- Azure ML workspace configuration with compute clusters, model deployment, and managed endpoints
- A/B testing and canary deployment patterns for safe model rollouts with traffic splitting
- Model monitoring setup: data drift detection, prediction quality tracking, and alert configuration
- MLOps CI/CD pipeline templates for automated model training, validation, and deployment
- Cost optimization strategies: spot/preemptible instances for training, auto-scaling for inference
- Model governance framework with versioning, lineage tracking, and approval workflows
Who This Is For
- ML Engineers deploying models to production cloud environments
- Data Scientists transitioning from experimentation to MLOps practices
- Platform teams building ML infrastructure for data science organizations
- Engineering leaders implementing responsible AI deployment practices
Why Choose Citadel
This playbook is written by engineers who have deployed ML systems serving millions of predictions daily across all three major cloud providers. Every pattern addresses the real challenges of production ML: reliability, cost, monitoring, and governance. You get deployment architectures that work at scale, not tutorials that stop at “model.predict()”.
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