September 2024
Intermediate to advanced
366 pages
9h 58m
German
Content preview from Aufbau von Pipelines für maschinelles Lernen
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Kapitel 10. Fortgeschrittenes TensorFlow Extended
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Mit den beiden vorangegangenen Kapiteln über den Einsatz von Modellen haben wir unseren Überblick über die einzelnen Pipeline-Komponenten abgeschlossen. Bevor wir in die Orchestrierung dieser Pipeline-Komponenten eintauchen, wollen wir in diesem Kapitel eine Pause einlegen und fortgeschrittene Konzepte von TFX vorstellen.
Mit den Pipeline-Komponenten, die wir bisher vorgestellt haben, können wir für die meisten Probleme Pipelines für maschinelles Lernen erstellen. Manchmal müssen wir jedoch unsere eigene TFX-Komponente oder komplexere Pipeline-Graphen erstellen. Deshalb werden wir uns in diesem Kapitel darauf konzentrieren, wie wir eigene TFX-Komponenten erstellen können. Wir leiten das Thema mit einer benutzerdefinierten Ingestion-Komponente ein, die Bilder direkt für Computer Vision ML-Pipelines einliest. Darüber hinaus stellen wir fortgeschrittene Konzepte von Pipelinestrukturen vor: die gleichzeitige Erzeugung von zwei Modellen (z. B. für den Einsatz von TensorFlow Serving und TFLite) sowie das Hinzufügen eines menschlichen Gutachters in den Pipelineworkflow.
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