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Machine Learning Ops (MLOps) is an area developing for the specific needs of machine learning. Especially when 6- to 7-figure dollar amounts and jobs can be at risk if an error occurs, you need a GitOps methodology and a way to leverage technologies such as service meshes. These will help you to update and test models faster and more frequently, while being able to make changes/rollback in a heavy, services-based architecture can cause unintended effects through the rest of the system. You need to control the blast radius of negative impacts and release the new models incrementally. This is known as “progressive delivery,” which includes strategies such as canarying, A/B testing, and incremental blue-green deployments.
Paul Curtis, Principal Solutions Architect at Weaveworks, will cover GitOps, Progressive Delivery that leverages service meshes, and their application to people with MLOps needs and concerns.
Benefits to the ecosystem:
Using the power of service meshes for machine learning ops is still fairly new, let alone applying GitOps and progressive delivery methodologies for reliability, lower risk, and control. We hope to bring useful tools and approaches to the right audiences looking to leverage service meshes to lower the risks of specific verticals.