Deep dives into agentic AI, LLM evaluation, synthetic data generation, model selection, and production AI engineering best practices.
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Explore how AI agents are revolutionizing supply chain management — from demand forecasting and inventory optimization to autonomous procurement and real-time logistics coordination.
A detailed technical comparison of the three leading AI agent frameworks in 2026 covering architecture, orchestration patterns, tool use, and production readiness.
Learn battle-tested error handling and graceful degradation patterns that keep AI agents reliable when LLM calls fail, tools break, or context windows overflow.
Understand the five levels of AI agent autonomy, from human-in-the-loop copilots to fully autonomous decision-making systems, and how to choose the right level for your use case.
JSONL is the standard data format for LLM fine-tuning. Learn why JSON Lines works best, how NeMo Curator processes raw data into JSONL, and best practices for training datasets.
NeMo Curator provides GPU-accelerated synthetic data generation pipelines for LLM training. Learn the Open QA, Writing, Math, and Coding pipelines with practical examples.
NeMo Curator's Domain Classifier and Quality Classifier use GPU-accelerated RAPIDS to split LLM training data into balanced, high-quality blends at terabyte scale.
Traditional data curation pipelines for LLM training face critical bottlenecks in synthetic data generation, quality filtering, and semantic deduplication across text, image, and video modalities.
