Deployment & Scaling
Gautam AI focuses on deployment and scaling strategies that transform trained models into reliable, secure, and scalable AI systems used in real-world enterprise environments.
What Is Deployment & Scaling?
Deployment and scaling cover the processes that take AI models from experimentation to production-ready services capable of handling real users, data, and performance constraints.
This stage ensures models remain reliable, cost-effective, and observable at scale.
Core Topics Covered
Model Deployment
APIs, containers, and inference services.
Scalability
Load balancing and horizontal scaling.
Performance Optimization
Latency, throughput, and cost control.
Monitoring & Logging
Metrics, drift detection, and alerts.
Security
Authentication, authorization, and data protection.
Lifecycle Management
Versioning, rollback, and continuous updates.
Who Should Learn Deployment & Scaling?
- AI engineers deploying real-world systems
- MLOps and platform engineers
- Teams operationalizing LLMs and copilots
- Researchers transitioning to production AI
What Comes After Deployment?
- Enterprise AI optimization & governance
- Continuous model improvement pipelines
- AI copilots & agent orchestration
- β-level professional & enterprise systems
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