jbaruch/speaker-toolkit

Six-skill presentation system: ingest talks into a rhetoric vault, run interactive clarification, generate a speaker profile, create presentations that match your documented patterns, produce the deck illustrations + thumbnail visual layer, and publish talk pages to a Jekyll shownotes site. Includes a 102-entry Presentation Patterns taxonomy (91 observable, 11 unobservable go-live items) for scoring, brainstorming, and go-live preparation.

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Impact

86%

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Average score across 26 eval scenarios

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layout:: talk

Decoding ML Pipelines: From CI to GPU

Name: jbaruch/speaker-toolkit
Rating: 86.63 (1 reviews)
Author: jbaruch

Conference: MLOpsCon 2026 Date: 2026-04-15 Slides: View Slides

A presentation at MLOpsCon 2026 in April 2026 in Berlin, Germany by {{ site.speaker.display_name | default: site.speaker.name }}

Abstract

Most ML pipelines fail not in training but at the boring edges — data validation, CI gates, GPU scheduling. This talk walks through three production failures and the dull infrastructure choices that would have prevented them. The argument: ship the boring stuff first, then the model.