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Core principles, production patterns, and system design for AI products.
Models, workflows, and product patterns for generative systems.
Foundational ML concepts, data preparation, and model evaluation.
Neural networks, training loops, and modern representation learning.
LLM capabilities, prompting, evaluation, and production usage patterns.
Prompt design patterns, instruction shaping, and output control.
Retrieval-augmented generation architecture and practical production design.
Autonomous workflows, tool use, planning, and orchestration patterns.
Model Context Protocol and agent-to-agent communication patterns.
Data pipelines, quality, and infrastructure for AI systems.
Model deployment, monitoring, release management, and operational discipline.
Visual AI systems, image pipelines, and applied perception workflows.
Professional growth, interview preparation, and role progression.