Deploying ML at scale with Kubernetes and TFX

Speaker: Rishit Dagli Abstract: There has been increasing popularity in the area of ML however deploying ML models at scale and being able to efficiently and consistently manage: serving model, infrastructure management, model versions, canary deployments, batch predictions, model metadata, monitoring data drifts, model routing, and A/B testing is a difficult task. I may say doing these steps correctly is more difficult than training a model for which a lot of literature is available. Enter Kubernetes and TensorFlow Extended. This talk will start off by giving some more clarity about the ML deployment steps as well as introduce TensorFlow Extended, an open-source tool that integrates quite well with Kubernetes and makes the deployment process consistent and easy. I also plan to talk about two more ideas in this talk: using Istio for model routing and A/B testing as well as showing the audience how they could best use their offloading pre-processing to server with Kubernetes. To top this off, this would be followed by a demo of deploying a model at scale. https://cndmday.com/events/cloud-native-data-management-day-live/

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