Limited training data means useless
"Over 7,765 images of the Salar de Pajonales collected from drone footage and 1,154 samples directly taken from the lakebed detecting microbes in the salt domes, rocks, and crystals were used to train the model. The software confirmed that these photosynthetic bacteria were concentrated in small areas that were near water sources."
So this model is now hopefully capable of detecting life that looks a lot like the life in this particular lake in one spot on our planet. It's not going to be too great at identifying microbial life in a different environment here, let alone life that could work very differently in an environment nothing like a high-altitude lake. So far, they've trained a model on one place. I haven't seen them proving that this lake is like everywhere else (we all know it's not) or that their model was able to find useful results from anywhere else.
Probably all they have right now is a model that can distinguish whether a collected sample is likely to look like life from this lake or some uninhabited different control data. That means it's likely to generate a large array of zeros when faced with other signatures of life if they haven't set it up so badly that it's frequently throwing out false positives.