Overview
Explore AI-powered information retrieval strategies with Women of Search experts. Analyze fine-tuning ML models, RAG pipelines, and reranking methods for optimal use in search applications.
Syllabus
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- Introduction to AI-Augmented Information Retrieval
-- Overview of traditional vs. AI-powered strategies
-- Importance of AI in modern search applications
-- Introduction to Women of Search experts
- Fine-Tuning Machine Learning Models for Information Retrieval
-- Understanding fine-tuning in ML
-- Techniques for fine-tuning models for optimal search
-- Evaluating performance improvements
- Retrieval-Augmented Generation (RAG) Pipelines
-- Concept and architecture of RAG pipelines
-- Integrating retrieval methods with generation models
-- Case studies of RAG applications in search systems
- Reranking Methods in AI-driven Search
-- Purpose of reranking in information retrieval
-- Common reranking algorithms and techniques
-- Implementing reranking for improved search accuracy
- Comparative Analysis of Strategies
-- Criteria for comparing AI retrieval strategies
-- Pros and cons of fine-tuning, RAG, and reranking
-- Selecting the best approach for specific search scenarios
- Case Studies and Practical Implementations
-- Real-world applications and success stories
-- Hands-on implementation exercises
-- Analyzing the impact of chosen strategies
- Ethical Considerations and Challenges
-- Addressing ethical concerns in AI-driven search
-- Mitigating biases in models and retrieval strategies
- Future Directions in AI-Augmented Information Retrieval
-- Emerging trends and technologies
-- Potential impact on various industries
- Course Summary and Key Takeaways
-- Recap of learning objectives and covered topics
-- Discussion on the future of AI in search applications
Taught by
Tags
On-Demand
