Member of Technical Staff, Modal Labs
Charles Frye is a Developer Advocate at Modal Labs with expertise in deep learning and neural networks. A UC Berkeley Ph.D. graduate in Neuroscience, he transitioned from biology to computer science and statistics. He previously served as a Deep Learning Educator at The Full Stack and Weights & Biases, where he created educational content on machine learning and Python programming. He's known for his talent in explaining complex quantitative concepts to non-experts.
Practitioners from the companies at the frontier of inference and model development sit down to unpack what application developers need to understand right now: why fine-tuning is quietly resurging under the name "RL," how smart teams are compressing inference costs by shaping smaller specialized models, who owns the model routing problem, and why inference capacity is structurally behind demand — possibly for years.
GPU Optimization for Data Scientists: Essential Knowledge from Silicon to PyTorch | Comprehensive guide to GPU architecture and optimization for modern machine learning workloads. Learn critical GPU concepts from hardware fundamentals to high-level frameworks, with focus on performance tuning for neural networks. Master practical techniques for optimizing system latency and throughput in popular ML frameworks including PyTorch, vLLM, and RAPIDS. Essential knowledge for data scientists and ML engineers working with GPU-accelerated workloads.
The next edition is programmed by practitioners. Tell us what you built and what you learned.
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