2026 Software Review: What Is TensorFlow? | Label Your Data

- The functionality of software updates. The new version TensorFlow 2.0, while built on user feedback, has certain challenges in the realization of the new functions. This makes it problematic to work on the compatibility between the first version of TensorFlow and TensorFlow 2.0. Commonly enough, the algorithms written in the first version might require complete makeover in the newer version of TensorFlow. - Reliability issues. While many may be tempted to continue working with the initial version of TensorFlow, it may be less secure and reliable. There were quite a few cases of memory leaks that significantly impeded and harmed the development process. - The complexity of syntax. While TensorFlow is built to communicate with the users on Python, its syntax is somewhat different from classic Python and may be confusing. There is a possibility to overcome the complexities by using TensorFlow superstructures like Keras. It allows a simpler approach to building ML models. - Crashes. Despite its benefits of speed and flexibility, TensorFlow is still prone to crashes, especially for heavier architectures. This can result in losing work and valuable time spent restarting the sessions. It’s a popular point of view that TensorFlow’s competitor, PyTorch is a somewhat easier alternative. ... … However, it’s not without its flaws. A tool the size of TensorFlow can be too bulky and complex to run smoothly if you don’t have a powerful enough computer. TensorFlow may be prone to crashes and there have been reliability issues. This tool also has certain compatibility issues with other Python packages. Besides, the complexity of Python syntax still makes it more of a professional type of software that is not really designed to be used by noobs and novices.