Exam Professional Machine Learning Engineer topic 1 question 273 discussion

TPU (Tensor Processing Units) VMs are specialized hardware accelerators designed by Google specifically for machine learning tasks. TPUv3 Pod slices offer high scalability and are excellent for distributed training tasks. The TPUEmbedding API is optimized for handling large volumes of categorical features, which fits your scenario with 100,000 categorical features. This option is likely to offer the fastest training times due to specialized hardware and optimized APIs for large-scale machine learning tasks.

Addressing Bottleneck: As data size increases, CPU-based training becomes increasingly slow. TPUs are specifically designed to address this challenge, significantly accelerating training. Large Categorical Features: TPUEmbedding API efficiently handles embedding lookups for a vast number of categorical features, a common characteristic of recommender system data.

Option C

My Answer: A: most scalable approach that also minimizes training time: TPU using TPUEmbeading API https://www.tensorflow.org/api_docs/python/tf/tpu/experimental/embedding/TPUEmbedding