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Despite its enormous potential to enable new applications, deep learning remains prohibitively expensive, difficult, and time-consuming for the vast majority of companies. Training DL models at scale is particularly challenging: training a single model can take days or weeks, and DL engineers are often forced to spend much of their time doing DevOps or writing boilerplate code to handle routine tasks like data loading, distributed training, or fault tolerance.
Despite its enormous potential to enable new applications, deep learning remains prohibitively expensive, difficult, and time-consuming for the vast majority of companies. Training DL models at scale is particularly challenging: training a single model can take days or weeks, and DL engineers are often forced to spend much of their time doing DevOps or writing boilerplate code to handle routine tasks like data loading, distributed training, or fault tolerance.
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