DRYML an Open Source Meta-Library for Machine Learning and More

DRYML aims to empower the Machine Learning practitioner to spend less time writing boilerplate code, and more time implementing new techniques. DRYML provides a model serialization framework along with serialization implementation for many common ML frameworks and model types, a framework for defining and training models on a specific problem, and a system to compare models from different ML frameworks on the same footing.
Students will leverage their knowledge across multiple ML frameworks such as TensorFlow, PyTorch, and sklearn to build new models, optimize them, and compare them across ML frameworks.
Instructor: Matthew Krafczyk, NCSA Research Scientist
Session Date: March 22, 2023