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Explore tools and approaches for safe, effective enterprise GenAI deployment, including finetuning, moderation guardrails, and sidecar systems for automated compliance.
Explore challenges and best practices for deploying GenAI applications in enterprise environments beyond basic CI prompt testing.
Optimize RAG systems automatically with AutoRAG. Boost performance by finding the ideal pipeline for your data and use-case through efficient evaluation of various modules.
Explore AI reliability challenges in production, survey techniques for improvement, and gain insights from 18 months of solving AI engineering reliability issues.
Explore challenges and solutions in deploying conversational AI to enterprises, focusing on accuracy optimization, LLMOps workflows, and infrastructure requirements for scalable implementation.
Explore strategies for scaling LLM applications from beta to production, addressing challenges and building adaptable AI infrastructure for evolving models and workflows.
Discover strategies for scaling LLMs in production, covering performance profiling, GPU optimization, and guardrail interactions with real-world examples.
Explore LLM fine-tuning techniques to reduce costs and improve performance. Learn to select base models, prepare datasets, run fine-tuning jobs, and evaluate results for specific AI tasks.
Explore function calling for LLMs to access structured data, enhancing RAG capabilities without vector databases. Learn to enrich tables with metadata for improved query performance.
Optimize deep learning model training with DeepView. Gain insights into performance bottlenecks, enhance resource utilization, and achieve significant throughput improvements through intuitive visualizations and targeted optimizations.
Explore techniques for evaluating Large Language Models and RAG pipelines in production, focusing on Valor, an open-source evaluation service for rigorous real-world testing.
Explore advanced RAG techniques for building superior chatbots. Learn hybrid search, query generation, result filtering, re-ranking, and chunking to enhance information retrieval and user interactions.
Explore data versioning strategies for generative AI, focusing on cost-effective techniques to minimize processing time and API calls while enhancing collaboration and efficient versioning of annotations and embeddings.
Learn to build differentiated AI systems using Metaflow, covering data engineering, model training, and inference for large-scale AI applications in production environments.
Learn to efficiently fine-tune and deploy large language models, optimizing performance and resource utilization for production environments.
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