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LLM Production

Deploy and operate LLMs in production environments

1
Model Selection Guide
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Choosing the right model

Learn how to choose between GPT-4, Claude, Gemini, Llama and other models for your use case

2
LLM Benchmarks
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MMLU, HumanEval & more

Understand how to interpret benchmarks like MMLU, HumanEval, HellaSwag, and compare models

3
Vector Databases
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Pinecone, Chroma, Weaviate

Learn about vector databases for semantic search and RAG applications

4
LLM Observability
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Monitoring & debugging

Implement logging, tracing, and monitoring for LLM applications in production

5
Cost Optimization
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Reduce API costs

Strategies for reducing LLM costs: caching, batching, model selection, and prompt optimization

6
API Integration Patterns
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Streaming, retries, errors

Best practices for integrating LLM APIs: streaming responses, retry logic, rate limiting

7
LLM Deployment
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FastAPI, Docker, K8s

Deploy LLM applications with FastAPI, Docker, and Kubernetes for scalability

8
Production Guardrails
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Safety in production

Implement content filters, input validation, and output sanitization for safe deployments

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