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Building scalable machine learning projects is challenging enough without the added burden of hidden or unpredictable infrastructure costs.

AI workflows often use containers as isolated environments per task, but each spin-up requires initializing dependencies, configuring models, and loading data, slowing down workflows that need rapid, repeated executions.

Developing and deploying AI models at scale is challenging. Many teams face obstacles like disconnected workflows, runaway or prohibitive infrastructure costs, and slow time-to-market for AI solutions.

Visit Union in the re:Invent Expo Hall as part of the NVIDIA booth #1620.

Reaching the amazing milestone of 50 million downloads, we asked Niels Bantilan, Pandera’s creator, to reflect on the journey of getting here and what might be next.

Reproducibility is the cornerstone of science and engineering: Without it, we can’t reliably build on top of each other’s findings and work.