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Explore key aspects of aligning LLMs and set up infrastructure for a versatile alignment pipeline. Learn data collection, prompt tuning, and workflow management techniques.
Explore advanced techniques for implementing traceability and observability in multi-step LLM systems, focusing on RAG systems and leveraging OpenTelemetry for deep insights.
Explore hybrid search techniques combining keyword and vector algorithms to enhance LLM performance, including metadata filtering, TF/IDF ranking, and advanced vector search methods.
Explore practical considerations for building private RAG applications using open source and custom LLMs. Learn about OpenLLM and optimize inference performance for RAG deployments.
Explore open-source multimodal embeddings for cross-modal search and MM-RAG. Learn techniques for integrating diverse data types, enhancing decision-making with real-time processing and LLMs.
Explore config-based development for LLMs, covering versioning, routing, and evaluation techniques to transition Generative AI apps from prototype to production efficiently.
Explore advanced LLM fine-tuning techniques, from specialized tuning to instruction enhancement. Learn dataset preparation, model optimization, and leverage powerful libraries for efficient fine-tuning and deployment.
Explore LLM benchmarks, evaluation metrics, and critical assessment techniques to understand model strengths and limitations in this comprehensive talk on AI performance evaluation.
Explore orchestrating RAG pipelines, optimizing retrieval strategies, and leveraging Canopy and Pinecone for scalable AI applications with billions of documents.
Explore LLM hallucinations, evaluation methodologies, and golden sets. Witness a live demo of Deepchecks' new LLM evaluation module for robust performance benchmarking and quality assurance.
Explore security challenges in AI and LLM applications, learn attack scenarios, and discover best practices for mitigating risks and maintaining application integrity.
Explore advanced RAG concepts: chunking for efficient retrieval, embeddings for text representation, and vector databases for optimized storage and retrieval in LLM applications.
Explore best practices for LLM-based app development, covering design considerations, technical aspects, and deployment strategies for optimal performance and scalability.
Explore open-source LLMs, compare with proprietary options, understand economic aspects, evaluate performance, and learn to operate and fine-tune models using dstack.
Explore LLMOps architecture: MLFlow for LLMs, Vector Databases, RAG strategies, and prompt evaluation. Gain insights into optimizing and deploying large language models effectively.
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