Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process.

A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken implementation. Perhaps you made a mistake when cleaning your data, leading to an incorrectly trained model.

It is a general rule in computer science that partial failures are harder to fix than complete failures. In this episode, Zayd Enam describes the different dimensions on which a machine learning model can develop an error. Zayd is a machine learning researcher at the Stanford AI Lab, so I also asked him about AI risk, job displacement, and academia versus industry.

Show Notes

Why ML is hard

The post Machine Learning is Hard with Zayd Enam appeared first on Software Engineering Daily.