Canonical Data Platform 2021 winter roundup

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Canonical Data Platform: that was 2021

It’s that time of the year again: many folks are panic buying cans of windscreen de-icer spray and thermal underwear, bringing pine trees into the front room and preparing to enjoy an extended break with the family. So we thought to ourselves, what better time than now to take a look back at the year gone by on the Canonical Data Platform?

Data Lab – Charmed Kubeflow warms up

In March, Aymen examined running an AI data lab on-premise, and surveyed the benefits of this approach as well as the tools and drivers to do so. We learned about cost reduction, easy compliance with data governance norms, and the platform tooling needed to build a cloud-agnostic, on-premise ready financial market forecasting platform for data-driven simulations and active trading.

In April, Rui took a look at how to set up a production-ready AI model scoring server infrastructure with Kubeflow’s KFServing engine. We learned how to set up auto-scaling rules for AI and ML scoring servers running Tensorflow, PyTorch, XGBoost, ScikitLearn, and ONNX workloads, and went a little deeper on configuring canary (a.k.a. blue/green) rollouts of new models, data preprocessing pipelines with Kubeflow, and took a little peek at model explainability too.

Also in April, Maciej took us through the process of deploying and configuring NVIDIA RAPIDS and NGC Containers on Ubuntu, as well as the design decisions behind his recommended setup.

In May, Rui went further into the world


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