MLOps Engineering at Scale

7 Serverless machine learning at scale

This chapter covers

• Using IterableDataset with AWS and other clouds
• Understanding GPUs for PyTorch programming
• Scaling up gradient descent with a GPU core
• Benchmarking the DC taxi data set using linear regression

In chapters 5 and 6, you learned about using PyTorch on a small scale, instantiating tensors consisting of a few hundred data values and training machine learning models with just a few parameters. The scale used in chapter 6 meant that to train a machine learning model, you could perform gradient descent with an assumption that the entire set of model parameters, along with the parameter gradients and the entire training data set, could easily fit in memory of a single node and thus be readily ...