TCPx-AI Benchmark for End-to-End ML Process | heise

The development and deployment of AI applications produces high-performance computer systems. Ergonomics is that it is not possible to optimize writing – by training algorithms on the inferences with its assessment. Who has a CPU system in an end-to-end machine learning pipeline, says about video.

The Entwicklung und de Einsatz-künstlicher Intelligenz (KI) bestht aus more Schritten: Zunächst wird das Modell andhand von Beispildaten trainiert. Die Optimierung der Antworten erolgt entweder selbständig (Unsupervised Learning) or with human Unterstützung (Supervised Learning).

When the system is installed, it cannot be that classification data is generated. The display can be performed as inference or inference. The ergebnisse must be continuously tested and evaluated, one of the continuous results of the AI ​​system is its operation. The process is performed as an End-to-End Machine Learning Pipeline network.

Videos have emerged showing the CPU system differences in the processing pipeline in the industry benchmark TPCx-AI.

Look at yourself,

• welche Schritte der TPCx-AI Benchmark umfasst
• who summarizes the individual phases of the ML Pipeline
• Welche Geschwindigkeitsvorteile Sie durch die Wahl een geeiigneten Plattform können