Webinar
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
Speaker
Dhairya Gandhi
Senior Software Engineer
Dhairya Gandhi is the Technical Lead for JuliaSimSurrogates at JuliaHub, specializing in JuliaSim AI. He develops machine learning models with physical constraints for scalable, reliable optimization tasks. His research, presented in NeurIPS, AIAA, and ACS, contributes to Julia's Machine Learning Ecosystem. Dhairya holds a degree in Electrical and Electronics Engineering from BITS Pilani.