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Learn how to train custom Small Language Models (SLMs) for production tasks using minimal data through an innovative AutoML approach that extends beyond traditional model selection to automated data preparation. Discover how organizations can deploy AI solutions locally on their own hardware while maintaining data privacy and reducing costs compared to cloud-based alternatives. Explore a novel training pipeline where larger AI "teacher" models automatically generate and refine training examples to create specialized "student" models, eliminating the traditional bottleneck of manually labeling thousands of training examples. Examine the structure of generated training data and master techniques for effectively controlling latent variables that define each datapoint to optimize model performance. See practical demonstrations of how this approach applies to real-world production tasks including request triage, API chat interfaces, and data transformations, making custom AI development accessible without requiring expert teams or months of development time.
