Modern CI/CD Machine Learning Workflows Using Julia

Machine learning models require frequent iteration and tracking, yet complex workflows can hinder development. This webinar explores how to apply Continuous Integration/Continuous Deployment (CI/CD) principles to machine learning using Julia’s Pkg ecosystem, Buildkite, GitHub, and MLflow. We’ll introduce JuliaSim’s Mass Experimentation Workflow to simplify model validation, boost reliability, and streamline scaling in production.

Participants will learn how Julia’s CI/CD-driven approach enhances transparency and reproducibility, enabling robust, scalable experimentation in ML workflows. This session is ideal for data scientists, ML engineers, and MLOps practitioners aiming to build efficient, production-ready models.

Key Takeaways:

• CI/CD Integration in ML Workflows  
• Julia Ecosystem for Scalable ML Development  
• Mass Experimentation Workflow with JuliaSim  
• Test-Driven Development for Reliable Models  
• Reproducible and Transparent ML Processes