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This course takes you on an end-to-end journey of an AI-enabled application, from prompt experimentation to production deployment, while bringing different personas together to collaborate on a single platform seamlessly.
- Understanding GenAI fundamentals, including tokens, context windows, and model behavior
- Experimenting with prompts and evaluating your first AI-enabled application
- Introducing an orchestration layer for standardized GenAI development
- Implementing Retrieval Augmented Generation (RAG) for knowledge-enhanced applications
- Building autonomous AI agents with tool-calling capabilities
- Deploying AI safety guardrails and implementing GenAI security practices
- Enabling observability with metrics, logging, and distributed tracing for GenAI systems
- Exploring small language models and multi-modal capabilities
- Optimizing models through quantization and compression techniques
- Implementing Models as a Service (MaaS) for scalable AI infrastructure
