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Explore strategies to prevent LLM hallucinations by combining them with knowledge bases. Learn to build reliable AI-driven applications using an LLM-powered support center example.
Explore experiment tracking in the age of LLMs with Neptune's CEO. Gain insights on managing ML experiments, prompt engineering, and achieving control and confidence in model development.
Learn practical methodologies for building consistent, structured, and compliant AI systems using open access models and LLM wrappers to navigate restrictions and delight customers.
Develop custom AI models using fine-tuning and data-driven techniques to create differentiated products solving previously unattainable problems.
Explore techniques for efficient LLM inference, including pseudo-labeling, knowledge distillation, pruning, and quantization. Learn to optimize model selection and performance within latency and budget constraints.
Explore AIShield.GuArdIan technology for secure LLM adoption in businesses, addressing compliance, ethics, and role-based usage while unlocking AI potential responsibly.
Explore key challenges and pitfalls in building LLM-based products, including costs, latency, and effective implementation strategies. Gain insights from industry experts on navigating the complexities of AI development.
Explore open-source LLMs as alternatives to proprietary models, addressing security and privacy concerns. Learn to deploy and operate these models efficiently using Aqueduct on cloud infrastructure.
Explore the latest trends in Generative AI, gaining practical insights for startups to leverage advancements and enhance products in this dynamic field.
Explore scientific prompt evaluation with Promptimize. Learn about open-sourcing, test suites, and AI challenges. Gain insights on prompt optimization, deterministic evaluation, and the evolving landscape of AI prompt engineering.
Discover strategies to eliminate bad data at the source, improve data quality ownership, and enhance observability in Analytics/ML stacks. Learn from experts in data engineering and product management.
Explore vector databases' role in LLM apps, focusing on solving data issues and democratizing access to this technology for efficient billion-scale vector search.
Learn hands-on techniques for evaluating language models, including data sourcing, automated metrics, and human evaluation. Gain practical tools for assessing LLM-based applications effectively.
Explore Daft, an open-source query engine revolutionizing multimodal data processing. Learn about efficient data storage, governance, and innovative developments in unstructured data management.
Explore open-source LLMs as privacy-preserving alternatives to closed models. Learn integration challenges, solutions, and benefits for secure AI application environments.
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