Guest
AIQC; Deep Learning Experiment Tracking With Multi-dimensional Pre/post-processing. Speaker: Layne Sadler Summary AIQC began as framework for deep learning experiment tracking to accelerate open science, but it turns out tracking is the easy part. In this talk, we'll explore how MLOps is really about data pre/post-processing. E.g. how use a validation split w heterogenous, multi-dimensional data on a sliding window that has been 10xfolded with 4 encoders, and decode predictions 3months later? AIQC does that. Description Audience AIQC was initially designed as a high level API for scientists to make deep learning accessible, but over time it was expanded to meet the needs of expert university practitioners - so everyone should be able to get value from this presentation. The problems we'll discuss are also boiled down to their simplest form. Problem Space Theory Solution Demo. We'll explore how to solve the following chronic problems, which are hardcoded into m
