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
