Machine learning (ML) is widely deployed in safety-critical systems (e.g. self-driving cars). Failures can have disastrous consequences in these systems, and so ensuring the reliability of its operations is important. Mutation testing is a popular method for assessing the dependability of applications and tools have recently been developed for ML frameworks. However, the focus has been on improving the quality of test data. We present an open source data mutation tool, TensorFlow Data Mutator (TF-DM), which targets different kinds of data faults for any ML program written in TensorFlow 2. TF-DM supports different types of data mutators so users can study model resilience to data faults. We explain how different fault models are mapped to mutators in TF-DM, and present a detailed evaluation and resiliency analysis of 6 ML models and 3 datasets.