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Explore knowledge distillation techniques for optimizing ML models in NLP. Learn extraction and transfer methods, target skill identification, and innovative data-centric approaches for efficient model training.
Explore model distillation techniques for efficient enterprise AI systems. Learn strategies, real-world applications, and best practices to optimize performance and minimize costs in natural language processing.
Accelerate data labeling for AI models using programmatic techniques. Learn to capture SME knowledge, improve label accuracy, and explore real-world use cases in banking, pharma, and healthcare.
Leverage NLP and RAG systems to extract insights from structured and unstructured data, generate actionable insights, and empower data-driven analysis without SQL.
Explore Task Me Anything platform for efficient multimodal model evaluation. Learn programmatic task generation and active learning techniques to create custom benchmarks for data science projects.
Streamline data labeling with Alfred, combining foundation models and weak supervision to create high-quality training datasets for academic research and machine learning projects.
Master RAG pipeline optimization for accurate, reliable LLM applications. Learn semantic document chunking, embedding techniques, and fine-tuning strategies. Leverage Snorkel Flow to accelerate development and overcome retrieval errors.
Explore LLM evaluation for enterprise applications, covering metrics, approaches, and tools to ensure accuracy, compliance, and suitability for organizational needs.
Learn to build a robust evaluation framework for enterprise LLMs using Snorkel Flow. Explore techniques for creating quality training data and analyzing performance across metrics and data slices.
Explore advanced LLM evaluation techniques for enterprise AI, including data-centric approaches, quality modeling, and fine-grained benchmarking to overcome common challenges and improve performance.
Discover efficient techniques for aligning large language models with enterprise applications using Snorkel Flow, streamlining the process of training data preparation and model adaptation.
Explore groundbreaking research extending weak supervision to new task types, including ranking, and its potential impact on AGI superalignment.
Explore fine-tuning methods for LLMs, learn to curate high-quality training data efficiently, and understand evaluation techniques for accurate enterprise deployment of specialized language models.
Discover enterprise LLM alignment techniques to improve AI chatbot accuracy and compliance. Learn to align AI assistants with industry standards and policies for enhanced performance.
Explore how follow-up differential descriptions enhance zero-shot image classification by tailoring class descriptions, improving performance and addressing ambiguities in datasets.
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