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Explore data-centric AI development approaches for adapting foundation models to complex use cases, focusing on techniques for pre-training, instruction-tuning, and task-specific fine-tuning.
Explore data-centric AI's role in simplifying entity extraction from semi-structured documents, overcoming PDF challenges, and leveraging foundation models for accelerated development.
Explore the creation of RedPajama, an open-source AI project, and its impact on the future of AI. Learn about data collection and training processes for building leading open-source models.
Explore techniques for improving LLM factual accuracy, including DSP framework and MLflow LLMOps tools, to create reliable AI applications for production use.
Explore advanced classification techniques using Snorkel Flow, addressing challenges in label schema, high cardinality, and multi-label problems while leveraging foundation models for production-ready solutions.
Explore Georgetown's approach to enhancing ML project workflows and fostering collaboration between data scientists and subject matter experts using Snorkel.
Explore practical workflows for building enterprise information extraction applications using Snorkel Flow, covering annotation, error analysis, and model-guided iteration for powerful sequence tagging models.
Explore Wayfair's ML strategies for enhancing e-commerce, from customer understanding to catalog enrichment. Learn about data-centric approaches and SME-DS collaboration for improved product discovery.
Explore how to leverage LLMs for extracting insights from text data in enterprise settings, addressing challenges and incorporating domain knowledge using Snorkel AI's solutions.
Explore data's journey across enterprises, unraveling its transformations and impact on decision-making, efficiency, and AI innovation through data-centric workflows.
Explore AI's impact on insurance: operational effectiveness, new business models, and partnerships. Learn about data-centric approaches for risk capacity matching and policy ingestion.
Explore AI's future with Yoav Shoham: task-specific models, product-algorithm fit, and the fusion of neural and symbolic approaches in language models and AI applications.
Explore advancements in computer vision for data-centric image classification. Learn about visual prompts, parameter-efficient models, and adapting representations for improved performance and efficiency.
Discover how Caterpillar combines domain knowledge and data to predict heavy equipment repairs, enhancing value for the company, dealers, and customers through modern analytics.
Explore DataComp, a benchmark for innovating AI training datasets. Learn how improved data curation outperforms model tweaks, enhancing multimodal AI performance with less compute.
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