12 citations · 36 across the 4 of their papers we have counts for
4 papers
tf.data service: A Case for Disaggregating ML Input Data Processing
Andrew Audibert, Yang Chen, Dan Graur +3
Machine learning (ML) computations commonly execute on expensive specialized hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt. For cost efficiency…
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Michael Kuchnik, Ana Klimovic, Jiri Simsa +2
Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipe…
tf.data: A Machine Learning Data Processing Framework
Derek G. Murray, Jiri Simsa, Ana Klimovic +1
Training machine learning models requires feeding input data for models to ingest. Input pipelines for machine learning jobs are often challenging to implement efficiently as they…
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Konstantinos, Katsiapis, Abhijit Karmarkar +17
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventu…