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Session

Developing and Deploying ML Across Teams with MLOps Automation Tool

We developed an MLOps automation tool in order to prevent ML engineers working on different projects from continuously repeating the same devops tasks manually. We standardized the management of cloud resources using infrastructure as code as well as model training and deployment workflows using best practices as code. Cloud agnostic infrastructure creation is performed using infrastructure as code templates rendered by the automation tool and Terraform, models are trained using MLflow with custom Kubernetes-backend plugins and a custom pipeline executor, models are deployed and monitored using rendered templates and Seldon-Core. Our MLOps tool allows machine learning engineers to perform DevOps tasks they were not trained to perform, standardize different projects, and save time and cost by reducing repeated work.


Speaker

Fabio Grätz, Ph.D.

Senior Machine Intelligence Engineer @Merantix

Fabio Grätz is the MLOps lead of Merantix Labs where he develops the machine learning model training and deployment infrastructure as well as Merantix' MLOps automation tool. Previously, he has worked as Senior Machine Learning Engineer at Merantix, leading multiple CV...

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Speaker

Thomas Wollmann

VP Engineering @Merantix

Thomas is VP of Engineering at Merantix Labs. He holds a PhD (Dr. rer. nat.) in Computer Science and a MSc in medical computer science from Heidelberg University. In his PhD thesis, he contributed to high-content microscopy image analysis by proposing various novel deep learning methods. His...

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Date

Wednesday May 26 / 09:10AM EDT (40 minutes)

Track

State of ML/AI Union

Topics

Machine LearningContinuous DeploymentContinuous DeliveryProgrammingDevops

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