TensorFlow

Thanks to TensorFlow: enabling Voxcruit to ensure trust, authenticity, and fairness across every AI-powered interview.

We chose TensorFlow.js for facial expression detection because it runs entirely in the browser, eliminating the need for server-side processing. That was critical for us to maintain EmotionSense’s core promise: full local processing and zero data exposure. Other libraries either lacked performance or required server dependencies that conflicted with our privacy-first approach.

Great LLM model to help us understand and analyze ingredients

TensorFlow provides a highly scalable and production-ready framework that works well across CPUs, GPUs, and TPUs. I appreciate its strong community support, ecosystem tools like TensorFlow Extended (TFX) and TensorBoard, and smooth deployment options on both mobile and cloud platforms.

TensorFlow Lite gave us the flexibility to experiment with AI-powered measurement models and sizing predictions. Its documentation is solid, and the community support made it easier to troubleshoot during development. We considered PyTorch and ONNX, but TensorFlow’s mobile integration and tooling felt more mature for our use case.

Made it possible to implement the VAD and Speech Recognition models on an Android device. Troubleshooting the issues that came up, however was.......an experience.

Running ML model on edge device is not easy but it is easier thanks to LiteRT. My notification spam filter app wouldn't be able to launch without this

We use TensorFlow.js for real-time image recognition directly in the browser. It powers the object recognition feature in AskCity — allowing users to identify urban objects on the go without uploading any data to external servers.

TensorFlow’s flexibility and extensive community support made it ideal for prototyping AI workflows, offering a robust alternative to frameworks like PyTorch due to its broader ecosystem and integration options for real-time processing.

I chose TensorFlow for segmentation tasks because it's a powerful, flexible, and well-supported open-source platform. It provides a wide range of tools and libraries for building, training, and deploying machine learning models. The support for deep learning architectures, efficient GPU acceleration, and a strong community make it ideal for complex tasks like image segmentation. Its scalability and integration with tools like Keras also streamline the development process.

TensorFlow is used as the core technology at Psycarenet to train LLM models specialising in psychology due to its robust deep learning infrastructure, scalability and efficiency in handling complex algorithms.

Alternatives I would like to try:

• pyTorch
• Hugging Face Transformers + Accelerate
• Microsoft DeepSpeed

Thanks to TensorFlow, we power intelligent recommendations by analyzing user preferences, helping surface the most relevant and engaging clips

Using TensorFlow's powerful ML capabilities enabled us to create accurate real-time pose estimation for push-up tracking and form validation.