End-to-end ML Workflow pt.1

The core of the workshop: attendees will apply the tools and techniques acquired thus far. In this and the following session, attendees will learn how to build an complete AI/ML pipeline, from data preparation, labeling, training, testing and real world applications. This session will be focused on the first part of the pipeline: data preparation and labeling.

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

Register