Training vs Inference: The ML Concept Most People Get Wrong | AI Simplified

Описание: In this short clip, AI expert Rahul Rai clears up a common misconception in the machine learning world: that training and inference are completely separate, "zero-one" processes.

Rahul explains that training involves feeding models structured, labeled data to help them identify patterns and correlations - essentially teaching the model to understand relationships within data. Meanwhile, inference occurs when the model encounters new, unlabeled data and must produce an output based on its training.

For Large Language Models (LLMs), inference looks like generating new content. When you ask an LLM to write a story, it draws on its training data - all the stories it has previously analyzed - to create something new that matches the style, structure, and thinking it learned.

This interconnected relationship between training and inference is fundamental to understanding how AI systems actually work in practice. Whether you're new to AI or a seasoned practitioner, grasping this nuance will deepen your understanding of machine learning systems.

Watch the full conversation for more insights on AI/ML fundamentals and practical applications in today's technology landscape.

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Machine Learning, AI Explained, ML Basics, Data Science, Tech Education, Inference Explained, AI Training, LLMs, Tech Talk, AI Simplified