  Industrial processes are monitored by a large number of various sensors that
produce time-series data. Deep Learning offers a possibility to create anomaly
detection methods that can aid in preventing malfunctions and increasing
efficiency. But creating such a solution can be a complicated task, with
factors such as inference speed, amount of available data, number of sensors,
and many more, influencing the feasibility of such implementation. We introduce
the DeTAVIZ interface, which is a web browser based visualization tool for
quick exploration and assessment of feasibility of DL based anomaly detection
in a given problem. Provided with a pool of pretrained models and simulation
results, DeTAVIZ allows the user to easily and quickly iterate through multiple
post processing options and compare different models, and allows for manual
optimisation towards a chosen metric.
