Handling NaN Values in SageMaker Linear Regression

Quick Answer

The error message here is unusually direct, it explicitly says the input data has NaN values, so this is really a data-quality problem being disguised as an algorithm error, not an issue with the SageMaker training job configuration itself. Linear regression, like many algorithms, requires every input to be a real number in order to compute its underlying matrix operations, so a missing value represented as NaN breaks that computation and causes the job to fail before meaningful training can even happen. The appropriate response is to handle the missing values during preprocessing, either by imputing them, commonly using a statistic like the mean or median of the column, or by removing the rows that contain them, both of which produce a dataset made entirely of valid numeric inputs that the algorithm can actually process. It's worth distinguishing this from a related but different situation, sparse data, where values are legitimately zero rather than missing, because converting to a sparse format solves a different problem and does nothing to eliminate an actual NaN value sitting in the dataset. Similarly, switching to a different algorithm doesn't inherently fix anything, since NaNs will still break most other algorithms; and adding more compute resources addresses capacity, not data validity, so it wouldn't touch the root cause here either. Whenever a training job error explicitly references missing or NaN values, treat it as a data preprocessing problem to solve with imputation or row removal, not an infrastructure or algorithm-selection problem.