Elliot Gunton
Senior Software Engineer at Pipekit.io
London, United Kingdom
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Elliot is a passionate maintainer of Hera, the Python SDK for Argo Workflows. At Pipekit, he is helping to bring scalable data pipelines to the Python world, unlocking the full potential of Argo Workflows for data scientists. Previously, at Bloomberg, Elliot supported Machine Learning engineers to accelerate their model retraining with Argo Workflows through Hera, simplifying the authoring of complex workflows.
Area of Expertise
Topics
Orchestrating Python Functions Natively in Argo Using Hera
Who has written an Argo Workflow spanning hundreds, if not thousands, of lines of YAML in a single file? And how much of it was unit tested? Integration tested? System tested only?! When extending the workflow, how was navigation through that YAML file? As we iterate on our Workflows and they grow into behemoths, we become less secure making changes and progress slows down, not to mention that working with YAML can hinder, rather than help, us.
Hera introduces native Python-functions-as-templates for your Workflows. Writing Workflows in Python is more intuitive and reflects the YAML spec of Argo through the use of context managers, so you’ll find yourself right at home coming from YAML! Hera offers a range of benefits over YAML, from all the natural benefits of Python modularization and packaging, to unit testing for individual functions and end-to-end tests of Workflows themselves.
Come learn how creating DAGs, loops, and more out of Python functions is made simpler by using Hera!
Argo For ML: Achieving Scalability and User Experience
This panel will feature several machine learning and MLOps engineers sharing their experiences using Argo Workflows. We will talk about the goals each company set out to achieve when building their machine learning platforms, the role Argo Workflows plays, and what to keep in mind when designing user interfaces for the team.
The panelists will highlight the use cases that Bloomberg, Dyno Therapeutics, and Centrica solve with Argo Workflows in the financial, bioinformatics, and energy sectors. The primary focus will be on the balancing act between achieving high scale and appropriately abstracting complexity so that each organization's ML teams can move quickly.
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