> For the complete documentation index, see [llms.txt](https://training.continuumlabs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://training.continuumlabs.ai/disruption.md).

# DISRUPTION

- [Data Architecture](https://training.continuumlabs.ai/disruption/data-architecture.md)
- [What is a data pipeline?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-a-data-pipeline.md)
- [What is Reverse ETL?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-reverse-etl.md): Combining generative AI with Reverse ETL in Modern Businesses
- [Unstructured Data and Generatve AI](https://training.continuumlabs.ai/disruption/data-architecture/unstructured-data-and-generatve-ai.md)
- [Resource Description Framework (RDF)](https://training.continuumlabs.ai/disruption/data-architecture/resource-description-framework-rdf.md)
- [Integrating generative AI with the Semantic Web](https://training.continuumlabs.ai/disruption/data-architecture/integrating-generative-ai-with-the-semantic-web.md)
- [Search](https://training.continuumlabs.ai/disruption/search.md): Generative AI and its relationship with search
- [BM25 - Search Engine Ranking Function](https://training.continuumlabs.ai/disruption/search/bm25-search-engine-ranking-function.md)
- [BERT as a reranking engine](https://training.continuumlabs.ai/disruption/search/bert-as-a-reranking-engine.md): Retrieval and Reranking
- [BERT and Google](https://training.continuumlabs.ai/disruption/search/bert-and-google.md)
- [Generative Engine Optimisation (GEO)](https://training.continuumlabs.ai/disruption/search/generative-engine-optimisation-geo.md): Navigating the New Frontier: A Guide to Generative Engine Optimisation
- [Billion-scale similarity search with GPUs](https://training.continuumlabs.ai/disruption/search/billion-scale-similarity-search-with-gpus.md)
- [FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://training.continuumlabs.ai/disruption/search/followir-evaluating-and-teaching-information-retrieval-models-to-follow-instructions.md)
- [Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/neural-collaborative-filtering.md): The highly popular 2017 paper that drove the advance of recommendation systems
- [Federated Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/federated-neural-collaborative-filtering.md): Collabative Filtering - Matching Consumers with Products and Services with Privacy
- [Latent Space versus Embedding Space](https://training.continuumlabs.ai/disruption/search/latent-space-versus-embedding-space.md)
- [Improving Text Embeddings with Large Language Models](https://training.continuumlabs.ai/disruption/search/improving-text-embeddings-with-large-language-models.md): Liang Wang and the Microsoft team
- [Recommendation Engines](https://training.continuumlabs.ai/disruption/recommendation-engines.md): There is major disruption coming to the recommendation engine industry
- [On Interpretation and Measurement of Soft Attributes for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/on-interpretation-and-measurement-of-soft-attributes-for-recommendation.md)
- [A Survey on Large Language Models for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/a-survey-on-large-language-models-for-recommendation.md)
- [Model driven recommendation systems](https://training.continuumlabs.ai/disruption/recommendation-engines/model-driven-recommendation-systems.md)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://training.continuumlabs.ai/disruption/recommendation-engines/recommender-ai-agent-integrating-large-language-models-for-interactive-recommendations.md)
- [Foundation Models for Recommender Systems](https://training.continuumlabs.ai/disruption/recommendation-engines/foundation-models-for-recommender-systems.md)
- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://training.continuumlabs.ai/disruption/recommendation-engines/exploring-the-impact-of-large-language-models-on-recommender-systems-an-extensive-review.md)
- [AI driven recommendations - harming autonomy?](https://training.continuumlabs.ai/disruption/recommendation-engines/ai-driven-recommendations-harming-autonomy.md): "Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach" by Ana Rita Gonçalves, Diego Costa Pinto, Saleh Shuqair, Marlon Dalmoro, and Anna S. Mattila
- [Logging](https://training.continuumlabs.ai/disruption/logging.md)
- [A Taxonomy of Anomalies in Log Data](https://training.continuumlabs.ai/disruption/logging/a-taxonomy-of-anomalies-in-log-data.md)
- [Deeplog](https://training.continuumlabs.ai/disruption/logging/deeplog.md)
- [LogBERT: Log Anomaly Detection via BERT](https://training.continuumlabs.ai/disruption/logging/logbert-log-anomaly-detection-via-bert.md)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://training.continuumlabs.ai/disruption/logging/experience-report-deep-learning-based-system-log-analysis-for-anomaly-detection.md)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://training.continuumlabs.ai/disruption/logging/log-based-anomaly-detection-with-deep-learning-how-far-are-we.md)
- [Deep Learning for Anomaly Detection in Log Data: A Survey](https://training.continuumlabs.ai/disruption/logging/deep-learning-for-anomaly-detection-in-log-data-a-survey.md)
- [LogGPT](https://training.continuumlabs.ai/disruption/logging/loggpt.md)
- [Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis](https://training.continuumlabs.ai/disruption/logging/adaptive-semantic-gate-networks-asgnet-for-log-based-anomaly-diagnosis.md)