Artificial Intelligence Development Frameworks
Learning Outcomes:
- Analyze and design AI framework architectures
- Apply AI frameworks
- Evaluate and optimize the performance of AI frameworks in real-world contexts
Topics:
- Architectural Foundations of AI Frameworks
- Deep Dive into Foundation Models
- Theoretical Evaluation Methodology
- Designing and Implementing Systematic AI Evaluation Pipelines
- Dataset Engineering and Advanced Prompt Engineering
- Contextual Optimization via Retrieval-Augmented Generation
- Agentic Patterns: Task-Oriented Planning and Tool Integration
- Strategic Model Adaptation and Inference Performance Optimization
- AI Integration within the Software Development Life Cycle
- Deployment, Observability, and AI Site Reliability Engineering
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