Cheuk Ting Ho
After having a career as a Data Scientist and Developer Advocate, Cheuk dedicated her work to the open-source community. Currently, she is working as a developer advocate for JetBrains. She has co-founded Humble Data, a beginner Python workshop that has been happening around the world. Cheuk also started and hosted a Python podcast, PyPodCats, which highlights the achievements of underrepresented members in the community. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.
Sessions
Python has become the most widely used language in scientific computing and data science due to its approachable syntax, vast ecosystem of libraries, and rapid prototyping capabilities. However, its interpreted nature often poses a performance bottleneck for computationally intensive tasks common in High Performance Computing (HPC) used in scientific and data work, such as large-scale simulations, complex data analysis, and machine learning model training.
Enhancing Python's performance, therefore, is critical for scientific computing: it allows researchers and engineers to maintain the productivity and flexibility of the Python environment while achieving the necessary speed and scalability to tackle demanding, real-world HPC problems without needing to switch to lower-level languages entirely.
Large Language Models (LLMs) are becoming central to modern applications, yet effectively evaluating their performance remains a significant challenge. How do you objectively compare different models, benchmark the impact of fine-tuning, or ensure your LLM responses adhere to safety guidelines (guard-railing)? This hands-on workshop addresses these critical questions.