Building Robust ML Production Systems Using OSS Tools for Continuous Delivery... Dr. Fabio M. Grätz
Channel: The Linux Foundation
Date: 2022-10-08

Building Robust ML Production Systems Using OSS Tools for Continuous Delivery for ML (CD4ML) - Dr. Fabio M. Grätz, Merantix Momentum

Teams living a DevOps culture leverage continuous integration & continuous delivery (CICD) to frequently and reliably release software, rapidly launch new features, and maintain robustness through testing and automation. In contrast to that, most ML teams test and deliver their models manually and infrequently, leading to long and error-prone delivery cycles. Data and hyper parameter configuration lack versioning, making lineage tracking and reproducing results difficult. ML has been described as a “High Interest Credit Card” as high maintenance cost can be incurred. To reduce this risk, continuous delivery for ML (CD4ML) extends CICD by introducing software best practices to ML: data, code, and hyper parameters are versioned, the process from data validation to model deployment is automated and tested. Statistical metrics of deployed models are monitored to trigger retraining if models decay. Fabio shows how ML-teams at Merantix Momentum live a MLOps culture and use vendor-agnostic OSS tools to build robust ML production systems for various industries: Flyte (workflow orchestration on Kubernetes), Mlfow, Squirrel (data mesh and versioning), Hydra (configuration), and seldon (deployment) are combined to build ML-systems that continuously train, test, deploy, and monitor models.