I currently have eight features which are either categorical or continuous variables. My targets are many (~1000) binary variables. So far I have attempted skmultilearn and sklearn.multioutput. I would like some help on developing a tensorflow model, or if its even possible. Any guidance is appreciated.
The term you would use for that is "multilabel classification".
It is certainly possible to do so in Tensorflow, although I would say it is more frequent to see it in examples of image classification rather than tabular data.
They key to this kind of task is that the final unnormalized log-probabilities (logits) go through a sigmoid layer, and then the binary cross-entropy is used as loss function.
Here you can find a complete example.
As a side note, when implementing it, you can compute both the sigmoid and the loss together with tf.nn.sigmoid_cross_entropy_with_logits.