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-# Roadmap
-
-Alibi Detect aims to be the go-to library for **outlier**, **adversarial** and **drift** detection in Python using 
-both the **TensorFlow** and **PyTorch** backends.
-
-This means that the algorithms in the library need to handle:
-* **Online** detection with often stateful detectors.
-* **Offline** detection, where the detector is trained on a batch of unsupervised or semi-supervised data. This assumption resembles a lot of real-world settings where labels are hard to come by.
-
-The algorithms will cover the following data types:
-* **Tabular**, including both numerical and categorical data.
-* **Images**
-* **Time series**, both univariate and multivariate.
-* **Text**
-* **Graphs**
-
-It will also be possible to combine different algorithms in ensemble detectors.
-
-The library **currently** covers both online and offline **outlier** detection algorithms for 
-tabular data, images and time series as well as offline **adversarial** detectors for 
-tabular data and images. Current **drift** detection capabilities cover almost any data modality such as mixed type tabular data, 
-text, images or graphs, both in the online and offline setting. Furthermore, Alibi Detect provides supervised drift and context-aware drift detectors.
-
-The **near term** focus will be on extending save/load functionality for PyTorch detectors, and adding outlier detectors for text and mixed data types.