Building AI-Powered Applications
Learn to build complete applications using RunAs AI as the core engine — architecture, integration, deployment, and observability.
Application architecture
Design patterns, service boundaries, and picking the right topology for AI-first apps.
Frontend integration
Embedding streaming, progressive responses, and rich UX patterns into web and mobile frontends.
Backend API design
Designing stable, idempotent, and scalable APIs for AI workloads and long-running jobs.
Database integration
Modeling, caching, and transactional patterns for storing model outputs and application state.
AI models & inference
Selecting models, serving topologies, and inference optimizations for production workloads.
Data pipelines & storage
ETL/ELT, event-driven ingestion, and dataset management for training and online features.
Scalability & deployment
Autoscaling, canaries, warm pools, and cost-aware deployment strategies for AI services.
Monitoring & analytics
Instrumenting latency, quality metrics, drift detection, and business KPIs for AI applications.
Security & compliance
Auth patterns, secret management, data governance, and privacy-first design for AI systems.
Project: end-to-end build
Hands-on project tying together architecture, frontend, backend, models, and monitoring.
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