Does WarpStream guarantee correct order inside partition only for acknowledged messages or also among the acknowledged messages (in different batches)? If so how do you keep clocks synchronized between the agents?
Related to 1. If I understood corrently the agent generates single object per each flushing interval containing all data accross all topics it has received. Does this mean that when reading the consumer needs to read multiple partition data simultaneously to access just single partition? How about scaling consumers horizontally how does WarpStream Agent handle horizontal partitioning of the stream from consuming side?
I have somehow feeling that this does not answer the original question. Could you give some examples of impossible or not easy tasks this achieves and other solutions don’t?
I'm little bit troubled by the model created for classifying the vein patterns. With only corpus of size 40 and using two classes without augmentation will most likely end up overfitting the model. I would say the model is currently learning to classify left or right hand but doesn't really care about the veins much.
Have you tried the performance with some other user?
I would also probably use data augmentation and ie. flip and rotate images, vary contrast etc. That might prevent some amount of overfitting.
With these kinds of problems usually classifying models are not very well suited. Basically with neural networks you are causing the manifold to partition whole output space and so you can expect that there is practically unlimited amount of different patterns that look equal to your hand (the class you have trained to be 'your hand')
For better model you need more data, it can be labeled of course but there is also unsupervised options you could consider such as autoencoders. With facial recognition the siamese networks and triplet loss based networks are pretty popular and you could maybe take a look into them.