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BásicocourseAcceso por bootcamp
LLM Access Strategies Guide
64
Lecciones
8
Módulos
🎓
Acceso por bootcamp
Lo que aprenderás
✓Apply a 5-dimension decision framework (cost, quality, privacy, speed, simplicity) to choose the optimal LLM provider
✓Integrate OpenAI API for production cloud applications with robust error handling
✓Run LLMs locally with LM Studio (GUI) at zero cost with full privacy
✓Deploy local LLMs in production with Ollama CLI and Docker containers
✓Access 100+ models from multiple providers through OpenRouter with cost optimization
✓Deploy LLMs serverless with Modal for auto-scaling without DevOps
✓Compare providers quantitatively with real benchmarks (latency, cost, quality)
✓Build a Unified AI Client that abstracts providers with automatic fallback strategies
¿Para quién es?
- •AI Engineers who need to decide how to access LLMs for real projects with informed trade-offs
- •Backend developers adding AI capabilities who want flexibility across providers
- •Startups seeking cost optimization and vendor diversification across LLM providers
- •Developers without credit cards or limited budgets who need free local alternatives
Requisitos
- •Basic Python (variables, functions, classes)
- •Basic HTTP knowledge (GET, POST requests)
- •Basic terminal usage (navigation, running commands)
- •No prior LLM or AI experience required
Contenido del curso
1Módulo 1: Decision Framework para Acceso a LLMs8 lecciones
2Módulo 2: OpenAI API8 lecciones
3Módulo 3: LM Studio (Local GUI)8 lecciones
4Módulo 4: Ollama (Local CLI)8 lecciones
5Módulo 5: OpenRouter (Agregador Multi-Provider)8 lecciones
6Módulo 6: Modal (Serverless Deployment)8 lecciones
7Módulo 7: Comparación Técnica y Decision Matrix8 lecciones
8Módulo 8: Unified AI Client (Proyecto Final)8 lecciones
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