Functional Open Science Skills for AI/ML Applications
When
Explore the essential components of Open Science, including reproducibility, version control with Git, the importance of workflows, and tools and resources such as Hugging Face. This session provides an introduction to the ecosystem that enables modern science to be collaborative, transparent, and scalable. Participants will learn of containers to ensure reproducibility, leveraging Git for version control, and applying platforms like Hugging Face for machine learning workflows.
This workshop series provides graduate students in public universities with developing skills and learning tools required in today's AI/ML-focused science.
Ranging from covering the basic moving parts to understanding AI's role in Open Science, this workshop aims to lend an understanding where to obtain compute, covering software environments and reproducibility, the role of workflows, and aiming to create an end-to-end Machine Learning (ML) workflow.
SERIES: Functional Open Science Skills for AI/ML Applications
Where: Register for Zoom Link
Instructor: Michele Cosi and Carlos Lizárraga
YouTube: UArizona DataLab and session links
- 1/28 The moving parts of Functional Open Science
- 2/4 AI's Role and Tools in Open Science
- 2/11 Learning to Work in the Cloud: JetStream2 and Reproducibility
- 2/18 Handling Images & Videos pt. 1
- 2/25 Handling Images & Videos pt. 2
- 3/4 Training and Testing Models
- 3/18 End-to-end ML Workflow pt.1
- 3/25 End-to-end ML Workflow pt.2