- Type
- Open-source ML platform
- Developed by
- Google Brain / Google
- Initial release
- 2015
- Written in
- Python, C++, CUDA
- Key tools
- Keras, TF Lite, TF.js, TFX
- Licence
- Apache 2.0
- Type
- Open-source ML platform
- Developed by
- Google Brain / Google
- Initial release
- 2015
- Written in
- Python, C++, CUDA
- Key tools
- Keras, TF Lite, TF.js, TFX
- Licence
- Apache 2.0
TensorFlow is an open-source machine learning platform developed by the Google Brain team and released publicly in 2015. It was one of the first widely adopted deep-learning frameworks and is designed to span the entire model lifecycle, from research experimentation to large-scale production deployment across data centres, mobile phones, web browsers, and embedded devices.
The name refers to the flow of tensors, multi-dimensional arrays, through a computational graph of mathematical operations. In its original design, TensorFlow used a static graph that was defined in full before execution, which enabled aggressive optimisation but made debugging harder. With TensorFlow 2.0, released in 2019, the platform adopted eager execution by default, bringing it closer to the interactive, line-by-line style popularised by PyTorch, while retaining the option to compile graphs for performance.
Keras and the high-level API
Since TensorFlow 2.0, the recommended way to build models is through Keras, a high-level API that is now tightly integrated as tf.keras. Keras lets developers assemble networks from layers using concise, readable code, define training with a few method calls, and switch between simple sequential models and complex custom architectures. This integration significantly improved TensorFlow's ease of use, which had been a common criticism of earlier versions.
Deployment ecosystem
TensorFlow's principal advantage is its mature deployment tooling. TensorFlow Serving provides a high-performance system for hosting models behind an API in production. TensorFlow Lite (now part of the LiteRT effort) compresses and optimises models to run on Android, iOS, microcontrollers, and other resource-constrained hardware, supporting on-device and edge inference. TensorFlow.js enables models to run directly in a web browser using JavaScript. TFX, or TensorFlow Extended, offers an end-to-end pipeline framework covering data validation, training, evaluation, and serving for managed machine-learning operations.
The platform runs on central processing units, GPUs, and Google's custom Tensor Processing Units, which are designed specifically to accelerate TensorFlow workloads.
Position in 2025 and 2026
The framework landscape has consolidated into two leaders. As of 2025, TensorFlow retained roughly 38 percent of overall framework usage and a strong position in large-scale enterprise deployment, while PyTorch led research and new model development. TensorFlow and PyTorch have converged in their core capabilities, and the practical decision between them now rests on existing infrastructure, deployment targets, and team familiarity. Google has continued to invest in its newer JAX stack for cutting-edge model training, while maintaining TensorFlow for its broad production install base.
| Capability | TensorFlow strength | |------------|--------------------| | Mobile and edge | TensorFlow Lite / LiteRT | | Browser | TensorFlow.js | | Production pipelines | TFX, TF Serving | | Hardware | CPU, GPU, TPU |
TensorFlow's deployment focus makes it relevant to Malaysian organisations that need to put models into production rather than only conduct research. Banks such as Maybank and CIMB, telecommunications providers including Maxis and Telekom Malaysia, and e-commerce platforms operating in the region run recommendation, fraud-detection, and customer-service models that benefit from mature serving infrastructure of the kind TensorFlow provides.
TensorFlow Lite is particularly significant for edge and mobile applications in Malaysia, where smartphone penetration is high and on-device inference reduces dependence on connectivity. Use cases include mobile banking security, agricultural apps that identify crop disease from phone cameras for the palm oil and smallholder sectors, and manufacturing quality-control systems in the Penang and Kulim electronics corridor that run vision models on factory-floor hardware.
The Malaysia Digital Economy Corporation (MDEC) promotes AI adoption among small and medium enterprises, and free, open frameworks such as TensorFlow lower the cost of entry. HRD Corp-funded training and university programmes at institutions including Universiti Teknologi Malaysia and Multimedia University cover TensorFlow alongside PyTorch.
Google Cloud's regional presence and the expansion of data-centre infrastructure in Johor and the Klang Valley give Malaysian developers access to TPU and GPU resources for training TensorFlow models, supporting national ambitions in the digital economy under the MyDigital Blueprint.