Ask any developer and they will tell how TensorFlow is the go-to library for deep learning projects. Its popularity for implementing deep learning techniques has only risen in the last few years. Before we understand how TensorFlow is changing AI modeling, let us have a look at what TensorFlow is. TensorFlow is a free and open-source software library that is often used for Machine Learning and Artificial Intelligence. While applications of TensorFlow can be used for a variety of tasks, it primarily focuses on training and understanding deep neural networks within AI algorithms.
TensorFlow is the go-to AI modeling framework for most developers. It is often used as a primary open-source tool or clubbed with PyTorch to develop ML, NLP, and Deep Learning models. This was further established by the latest O’Reilly surveys wherein more than 60% of data scientists are using TensorFlow as their primary Tool while PyTorch was a second favorite choice.
Enhancements to TensorFlow’s ecosystem
TensorFlow’s team of developers is constantly evolving the library. Each version launched brings something new to the table for developers to code and innovate with. The latest introduction in the TensorFlow ecosystem is the TensorFlow Lite plugin for Flutter, also launched on TensorFlow GitHub Account. Developed by Indian contributor, Amish Garg, the plugin has risen in popularity exponentially ever since its launch. This rise in popularity led the company to migrate the plugin to an official platform to be managed directly by the Google team. Individual contributors from the community have enhanced and updated the plugin to the latest version accompanied by innovative features like object detection through live camera feed.
TensorFlowLite is an efficient platform for running TensorFlow models on local devices, mobiles, and embedded web devices. Its cross-platform features and optimizations have made the tool a great addition to Flutter’s development toolbox.
Additional feature of TFLite’s platform is the reduced memory requirements compared to other models. By sharing buffers between the different tensors, the platform facilitates running deep learning models on smaller edge devices. It optimizes the tool’s memory initialization enabling users to make the best of low memory usage and smaller overhead memory allocation.
Another powerful addition to TensorFlow Lite platform is a microcontroller board by the name Dev Board Micro. Small, power-efficient, and equipped with Coral Edge TPU on board, this microcontroller offers higher inference speeds for Larger TFLite models.
Existing TensorFlow tools for specialized AI development needs
Developers of TensorFlow get to work with a diverse range of innovative projects that push AI’s boundaries from every direction. This is enabled by many existing tools, extensions, and add-ons that are already part of TensorFlow’s ecosystem.
- Graphics layers and viewer
- Browser-based, domain-specific libraries
- Distributed deep-learning language
- NLP frameworks and models
- Probabilistic programming library
- RL (Reinforcement Learning) development library
- AI modeling tool for quantum computing environments
- Model fairness-metric visualization tool
TensorFlow: Superior tool for AI development
TensorFlow is bound to maintain a monopoly in the ML learning libraries market. This has been further established by recently acquired funding from Google and its parent, Alphabet, in the TensorFlow Ecosystem. The library is fundamentally crucial in building mission-critical AI projects and is only bound to be enhanced by newer updates and features.
