2024-07-12 –, For Loop (3.2)
Machine learning model development, characterized by iterative experimentation and adjustments, often leads to complex model iterations, making tracking and debugging challenging. This talk explores the application of CI/CD methodologies to machine learning, using Julia's Pkg ecosystem, Buildkite, GitHub, and MLflow. We showcase a streamlined process for efficient model development and tracking that can lead to mass robust experimentation for machine learning workflows
By reimagining machine learning model development through the lens of package development, we introduce a stream-lined workflow that enables building machine learning models in a test-driven manner with git-level reproducibility all within Julia. This approach minimizes the hidden states and dependencies often encountered in traditional machine learning development environments. We will explore in depth how this reconfiguration leads to more robust, reproducible, and streamlined model development processes that can scale to production environments.
Complementing this, we integrate MLflow, an open-source model lifecycle management tool, through MLFlowClient.jl and extend it to enhance our development pipeline. MLflow's graphical user interface facilitates easy management of model configurations and their outcomes. It provides valuable insights and a user-friendly interface for tracking, managing, and evolving machine learning models.
Throughout the session, we showcase how these methodologies, when applied in the realm of machine learning, lead to more efficient, transparent, and robust model development, enabling mass robust experimentation for machine learning workflows in the julia ecosystem.
