# TensorFlow Lite Published articles for TensorFlow Lite. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Codename One Adds On-Device AI and Loopback MCP Support DevFeed: [Codename One Adds On-Device AI and Loopback MCP Support]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-08-02T00:00:00Z Content type: release Language: en Sources: [CodeName One]() Topics: [On-device AI](), [Model Context Protocol](), [Artificial Intelligence](), [LiteRT](), [Inference](), [Mobile](), [supply-chain-security]() Tags: [applications](), [inference](), [litert](), [mcp](), [mobile](), [on-device](), [on-device-ai](), [tensorflow-lite]() ### AI overview Codename One has added on-device vision, language, and LiteRT inference to its core, alongside a guarded loopback MCP transport for inspecting and operating applications. The article explains platform support, asynchronous OCR, model sessions, secure model downloads, and privacy limitations of local inference. ### Source excerpt Codename One now exposes on-device vision, language, and LiteRT inference in the core, while a guarded loopback MCP transport lets an LLM inspect and drive real applications. ## Gesture Recognition Based on TFLite DevFeed: [Gesture Recognition Based on TFLite]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [TensorFlow Lite](), [Espressif](), [model-deployment](), [Tensorflow](), [Keras](), [On-device AI]() Tags: [ai](), [blog](), [deep-learning](), [deployment](), [development-board](), [edge-ai](), [edge-computing](), [esp-idf](), [espressif](), [how-to](), [inference](), [keras](), [model-deployment](), [model-training](), [tensorflow-lite]() ### AI overview This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference. ### Source excerpt This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI. ## What's new in TensorFlow 2.20 DevFeed: [What's new in TensorFlow 2.20]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2025-08-19T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [LiteRT](), [On-device AI](), [Inference](), [Latency](), [IO](), [GPU](), [data](), [Google](), [Cloud](), [Python](), [C++](), [Kotlin]() Tags: [announcement](), [c-plus-plus](), [data](), [google](), [gpu](), [inference](), [io](), [kotlin](), [latency](), [litert](), [npu](), [on-device](), [python](), [tensorflow](), [tensorflow-core](), [tensorflow-lite]() ### AI overview TensorFlow 2.20 introduces the independent LiteRT repository as the future home for on-device inference, replacing the deprecated tf.lite module. LiteRT adds Kotlin and C++ APIs, improves NPU and GPU acceleration, and supports lower-latency inference with fewer memory copies. The release also adds tf.data input-pipeline warm-up controls and makes Google Cloud Storage support optional. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.20 has been released! For ongoing updates related to the multi-backend Keras, please note that all news and releases, starting with Keras 3.0, are now published directly on keras.io. You can find a complete list of all changes in the full release notes on GitHub. tf.lite is being replaced by LiteRT The tf.lite module will be deprecated with development for on-device inference moving to a new, independent repository: LiteRT. The new APIs are available in Kotlin and C++. This code base will decouple from the TensorFlow repository and tf.lite will be removed from future TensorFlow Python packages, so we encourage migration of projects to LiteRT to receive the latest updates. More details to follow. As announced at Google I/O '25, LiteRT improves upon TFLite, particularly for NPU and GPU hardware acceleration and performance for on-device ML and AI applications. LiteRT provides a unified interface for Neural Processing Units (NPUs), removing the need to navigate vendor-specific compilers or libraries. This approach avoids many device-specific complications, boosts performance for real-time and large-model inference, and minimizes memory copies through zero-copy hardware buffer usage. For more information on the new repository and to sign up for the NPU Early Access Program, please reach out to the team at g.co/ai/LiteRT-NPU-EAP. Faster input pipeline warm-up with tf.data To help reduce latency, especially the time it takes for your model to process the first element of a dataset, we've added autotune.min_parallelism in tf.data.Options. This new option allows asynchronous dataset operations like .map and .batch to immediately start with a specified minimum level of parallelism, speeding up the initial warm-up time for your input pipelines. Changes to I/O GCS filesystem package The tensorflow-io-gcs-filesystem package for Google Cloud Storage support is now optional. Previously, it was installed, by default, with TensorFlow. If y ## Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications DevFeed: [Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-12-05T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [Training AI Models](), [machine learning overfitting]() Tags: [ai](), [benchmarks](), [community](), [computer-vision](), [data-quality](), [datasets](), [edge](), [google-ai](), [litert](), [machine-learning](), [on-device-ai](), [person-detection](), [research](), [tensorflow-lite](), [training]() ### AI overview Wake Vision is a roughly 6-million-image dataset for TinyML person detection. It provides large and quality-focused training sets, emphasizing that accurate labels can be especially valuable for constrained models. ### Source excerpt Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi - Harvard University TinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in--a new dataset designed to accelerate research and development in TinyML. Why TinyML Needs Better Data The development of TinyML requires compact and efficient models, often only a few hundred kilobytes in size. The applications targeted by standard machine learning datasets, like ImageNet, are not well-suited for these highly constrained models. Existing datasets for TinyML, like Visual Wake Words (VWW), have laid the groundwork for progress in the field. However, their smaller size and inherent limitations pose challenges for training production-grade models. Wake Vision builds upon this foundation by providing a large, diverse, and high-quality dataset specifically tailored for person detection--the cornerstone vision task for TinyML. What Makes Wake Vision Different? Wake Vision is a new, large-scale dataset with roughly 6 million images, almost 100 times larger than VWW, the previous state-of-the-art dataset for person detection in TinyML. The dataset provides two distinct training sets: Wake Vision (Large): Prioritizes dataset size. Wake Vision (Quality): Prioritizes label quality. Wake Vision's comprehensive filtering and labeling process significantly enhances the dataset's quality. Why Data Quality Matters for TinyML Models In traditional overparameterized models, it is widely believed that data quantity matters more than data quality, as an overparameterized model can adapt to errors in the training data. But according to the image below, TinyML tells a different story: The figure above shows that high-quality labels (less error) are more beneficial for under-parameterized mo ## Faster Dynamically Quantized Inference with XNNPack DevFeed: [Faster Dynamically Quantized Inference with XNNPack]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-04-09T16:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [Inference Performance](), [quantization](), [TensorFlow Lite](), [cpu](), [Machine learning](), [model-deployment]() Tags: [ai](), [announcement](), [article](), [cpu](), [inference](), [inference-performance](), [learn](), [ml](), [performance](), [precision](), [quantization](), [range](), [scale](), [tensorflow](), [tensorflow-lite](), [tensors]() ### AI overview This article explains how dynamic range quantization for XNNPack's Fully Connected and Convolution 2D operators improves CPU inference performance in TensorFlow Lite. The approach quantizes weights to 8-bit integers during model conversion and dynamically quantizes activations during inference, delivering most of the performance benefits of full quantization while retaining higher overall accuracy. The article reports a fourfold performance improvement over the single-precision baseline and describes how this can enable AI-powered features on older and lower-tier devices. ### Source excerpt Posted by Alan Kelly, Software Engineer We are excited to announce that XNNPack's Fully Connected and Convolution 2D operators now support dynamic range quantization. XNNPack is TensorFlow Lite's CPU backend and CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority. We quadrupled inference performance in TensorFlow Lite's XNNPack backend compared to the single precision baseline by adding support for dynamic range quantization to the Fully Connected and Convolution operators. This means that more AI powered features may be deployed to older and lower tier devices. Previously, XNNPack offered users the choice between either full integer quantization, where the weights and activations are stored as signed 8-bit integers, or half-precision (fp16) or single-precision (fp32) floating-point inference. In this article we demonstrate the benefits of dynamic range quantization. Dynamic Range Quantization Dynamically quantized models are similar to fully-quantized models in that the weights for the Fully Connected and Convolution operators are quantized to 8-bit integers during model conversion. All other tensors are not quantized, they remain as float32 tensors. During model inference, the floating-point layer activations are converted to 8-bit integers before being passed to the Fully Connected and Convolution operators. The quantization parameters (the zero point and scale) for each row of the activation tensor are calculated dynamically based on the observed range of activations. This maximizes the accuracy of the quantization process as the activations make full use of the 8 quantized bits. In fully-quantized models, these parameters are fixed during model conversion, based on the range of the activation values observed using a representative dataset. The second difference between full quantization and dynamic range quantization is that the output of the Fully ## Half-precision Inference Doubles On-Device Inference Performance DevFeed: [Half-precision Inference Doubles On-Device Inference Performance]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-29T18:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Inference Performance](), [On-device AI](), [LiteRT](), [cpu](), [AI, ML & Data Engineering]() Tags: [ai](), [announcement](), [benchmarks](), [cpu](), [how-to](), [inference](), [inference-performance](), [learn](), [on-device](), [tensorflow-lite]() ### AI overview TensorFlow Lite and XNNPack add generally available FP16 inference for supported ARM CPUs, reporting close to 2x speedups for floating-point models in production and benchmarked mobile and laptop environments. ### Source excerpt Posted by Marat Dukhan and Frank Barchard, Software Engineers CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority, and we are excited to announce that we doubled floating-point inference performance in TensorFlow Lite's XNNPack backend by enabling half-precision inference on ARM CPUs. This means that more AI powered features may be deployed to older and lower tier devices. Traditionally, TensorFlow Lite supported two kinds of numerical computations in machine learning models: a) floating-point using IEEE 754 single-precision (32-bit) format and b) quantized using low-precision integers. While single-precision floating-point numbers provide maximum flexibility and ease of use, they come at the cost of 4X overhead in storage and memory and exhibit a performance overhead compared to 8-bit integer computations. In contrast, half-precision (FP16) floating-point numbers pose an interesting alternative balancing ease-of-use and performance: the processor needs to transfer twice fewer bytes and each vector operation produces twice more elements. By virtue of this property, FP16 inference paves the way for 2X speedup for floating-point models compared to the traditional FP32 way. For a long time FP16 inference on CPUs primarily remained a research topic, as the lack of hardware support for FP16 computations limited production use-cases. However, around 2017 new mobile chipsets started to include support for native FP16 computations, and by now most mobile phones, both on the high-end and the low-end. Building upon this broad availability, we are pleased to announce the general availability for half-precision inference in TensorFlow Lite and XNNPack. Performance Improvements Half-precision inference has already been battle-tested in production across Google Assistant, Google Meet, YouTube, and ML Kit, and demonstrated close to 2X speedups across a wide range of ne ## Building a board game with the TFLite plugin for Flutter DevFeed: [Building a board game with the TFLite plugin for Flutter]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-18T17:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Inference](), [Reinforcement learning](), [Tensorflow](), [TensorFlow Agents](), [cross-platform](), [Mobile](), [board-game](), [Android](), [iOS]() Tags: [android](), [building](), [cross-platform](), [flutter](), [games](), [how-to](), [inference](), [ios](), [learn](), [mobile](), [plugin](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tensorflow-lite](), [tutorial](), [tutorials]() ### AI overview This tutorial shows how to port a TensorFlow Lite reinforcement-learning board game to Flutter. It loads a converted model, runs inference to choose the next move, and renders the game for Android and iOS using the Flutter frontend and TensorFlow Lite plugin. ### Source excerpt Posted by Wei Wei, Developer Advocate In our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Lite, we demonstrated how to train a reinforcement learning (RL) agent with TensorFlow, TensorFlow Agents and JAX respectively, and then deploy the converted TFLite model in an Android app using TensorFlow Lite, to play a simple board game 'Plane Strike'. While these end-to-end tutorials are helpful for Android developers, we have heard from the Flutter developer community that it would be interesting to make the app cross-platform. Inspired by the officially released TensorFlow Lite Plugin for Flutter recently, we are going to write one last tutorial and port the app to Flutter. Since we already have the model trained with TensorFlow and converted to TFLite, we can just load the model with TFLite interpreter: void _loadModel() async { // Create the interpreter _interpreter = await Interpreter.fromAsset(_modelFile); } Then we pass in the user board state and help the game agent identify the most promising position to strike next (please refer to our previous blog posts if you need a refresher on the game rules) by running TFLite inference: int predict(List> boardState) { var input = [boardState]; var output = List.filled(_boardSize * _boardSize, 0) .reshape([1, _boardSize * _boardSize]); // Run inference _interpreter.run(input, output); // Argmax double max = output[0][0 ]; int maxIdx = 0; for (int i = 1; i < _boardSize * _boardSize; i++) { if (max < output[0][i]) { maxIdx = i; max = output[0][i]; } } return maxIdx; } That's it! With some additional Flutter frontend code to render the game boards and track game progress, we can immediately run the game on both Android and iOS (currently the plugin only supports these two mobile platforms). You can find the complete code on GitHub. If you want to dig digger, there ar ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### AI overview The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms. ### Source excerpt Posted by Paul Ruiz, Developer Relations Engineer We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future _loadModel() async { final opt ## Simpleperf case study: Fast initialization of TFLite's Memory Arena DevFeed: [Simpleperf case study: Fast initialization of TFLite's Memory Arena]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-09T16:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Lite](), [Optimization](), [Machine learning](), [Android](), [Development](), [Code]() Tags: [adb](), [cache](), [display](), [models](), [performance](), [performance-optimization](), [profiling](), [reduce](), [tensorflow-lite](), [visualization]() ### AI overview This developer article presents a case study on optimizing TensorFlow Lite memory arena initialization. It explains how to profile an on-device machine-learning pipeline with Simpleperf, generate profile data, visualize it with pprof, and identify runtime bottlenecks such as ArenaPlanner::ExecuteAllocations. ### Source excerpt Posted by Alan Kelly, Software Engineer One of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite's memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today's article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit of low memory usage with little additional overhead. ML is normally deployed on-device as part of a larger pipeline. TFLite is used because it's fast and lightweight, but the rest of the pipeline must also be fast. Profiling on the target device with representative data lets us identify the slowest parts of the pipeline so that we can optimize the most important part of the code. In this article, I will describe the profiling and optimization of TFLite's memory arena with instructions on how to use Simpleperf and visualize the results. Sample commands are given. It is assumed that the Android NDK is installed and that you have a development device that you can connect to using adb. Simpleperf Simpleperf comes with some scripts to make it easier to use. run_simpleperf_on_device.py pushes simpleperf to the device and runs your binary with the given arguments. /usr/lib/android-ndk/simpleperf/run_simpleperf_on_device.py record -call-graph fp /data/local/tmp/my_binary arg0 arg1 ... This will generate the output file perf.data which you must then copy back to your computer. adb pull /data/local/tmp/perf.data You then generate the binary cache which contains all the information needed later to generate a useful profile. /usr/lib/android-ndk/simpleperf/binary_cache_builder.py -lib /your/binarys/folder -i perf.data And generate the proto buffer used for visualization: /usr/lib/android-ndk/simpleperf/pprof_proto_generator.py --ndk_path=/path/to/android-ndk -i perf.data -o profile.proto You can then display the results this using pprof: pprof -http :8888 profile.proto And open localhos ## What's new in TensorFlow 2.13 and Keras 2.13? DevFeed: [What's new in TensorFlow 2.13 and Keras 2.13?]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-07-25T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [Keras](), [TensorFlow Lite](), [datasets](), [API]() Tags: [apple](), [datasets](), [google](), [keras](), [mac](), [python](), [quantization](), [release](), [tensorflow](), [tensorflow-core](), [tensorflow-lite](), [usability]() ### AI overview TensorFlow 2.13 and Keras 2.13 introduce Apple Silicon wheels, make the Keras V3 format the default for .keras files, and add improvements to TensorFlow Lite, tf.data, dataset padding, and CPU BF16 execution. ### Source excerpt Posted by the TensorFlow and Keras Teams TensorFlow 2.13 and Keras 2.13 have been released! Highlights of this release include publishing Apple Silicon wheels, the new Keras V3 format being default for .keras extension files and many more! TensorFlow Core Apple Silicon wheels for TensorFlow TensorFlow 2.13 is the first version to provide Apple Silicon wheels, which means when you install TensorFlow on an Apple Silicon Mac, you will be able to use the latest version of TensorFlow. The nightly builds for Apple Silicon wheels were released in March 2023 and this new support will enable more fine-grained testing, thanks to technical collaboration between Apple, MacStadium, and Google. tf.lite The Python TensorFlow Lite Interpreter bindings now have an option to use experimental_disable_delegate_clustering flag to turn-off delegate clustering during delegate graph partitioning phase. You can set this flag in TensorFlow Lite interpreter Python API interpreter = new Interpreter(file_of_a_tensorflowlite_model, experimental_preserve_all_tensors=False) The flag is set to False by default. This is an advanced feature in experimental that is designed for people who insert explicit control dependencies via with tf.control_dependencies() or need to change graph execution order. Besides, there are several operator improvements in TensorFlow Lite in 2.13 add operation now supports broadcasting up to 6 dimensions. This will remove explicit broadcast ops from many models. The new implementation is also much faster than the current one which calculates the entire index for both inputs the the input instead of only calculating the part that changes. Improve the coverage for 16x8 quantization by enabling int16x8 ops for exp, mirror_pad, space_to_batch_nd, batch_to_space_nd Increase the coverage of integer data types enabled int16 for less, greater_than, equal, bitcast, bitwise_xor, right_shift, top_k, mul, and int16 indices for gather and gather_nd enabled int8 for floor_div and floor_m ## On-device fetal ultrasound assessment with TensorFlow Lite DevFeed: [On-device fetal ultrasound assessment with TensorFlow Lite]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-06-20T16:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Lite](), [Google](), [Artificial Intelligence](), [Inference](), [Mobile](), [Protocol (disambiguation)]() Tags: [ai](), [devices](), [google](), [health](), [healthcare](), [mobile](), [on-device](), [open-source](), [predictions](), [protocol](), [smartphones](), [tensorflow-lite]() ### AI overview This article explains how TensorFlow Lite and Google Research AI models are being used with portable, smartphone-integrated ultrasound devices to help health workers assess gestational age and fetal presentation in under-resourced settings. The models analyze ultrasound video collected through a blind sweep protocol and are intended to expand access to clinically useful prenatal screening. ### Source excerpt Posted by Angelica Willis and Akib Uddin, Health AI Team, Google Research How researchers at Google are working to expand global access to maternal healthcare with the help of AI TensorFlow Lite* is an open-source framework to run machine learning models on mobile and edge devices. It's popular for use cases ranging from image classification, object detection, speech recognition, natural language tasks, and more. From helping parents of deaf children learn sign language, to predicting air quality, projects using TensorFlow Lite are demonstrating how on-device ML could directly and positively impact lives by making these socially beneficial applications of AI more accessible, globally. In this post, we describe how TensorFlow Lite is being used to help develop ultrasound tools in under-resourced settings. Motivation According to the WHO, complications from pregnancy and childbirth contribute to roughly 287,000 maternal deaths and 2.4 million neonatal deaths worldwide each year. As many as 95% of these deaths occur in under-resourced settings and many are preventable if detected early. Obstetric diagnostics, such as determining gestational age and fetal presentation, are important indicators in planning prenatal care, monitoring the health of the birthing parent and fetus, and determining when intervention is required. Many of these factors are traditionally determined by ultrasound. Advancements in sensor technology have made ultrasound devices more affordable and portable, integrating directly with smartphones. However, ultrasound requires years of training and experience, and, in many rural or underserved regions, there is a shortage of trained ultrasonography experts, making it difficult for people to access care. Due to this global lack of availability, it has been estimated that as many as two-thirds of pregnant people in these settings do not receive ultrasound screening during pregnancy. Expanding access by enabling non-experts Google Research is building AI m ## Enhance your TensorFlow Lite deployment with Firebase DevFeed: [Enhance your TensorFlow Lite deployment with Firebase]() Original publisher: [Read original article]() Author: Khanh LeViet Published: 2020-06-26T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [TensorFlow Lite](), [Deployment](), [Firebase ML](), [model-deployment](), [Mobile]() Tags: [android](), [deployment](), [firebase](), [firebase-ml](), [google](), [inference](), [ios](), [machine-learning](), [ml-kit](), [remote-config](), [tensorflow-lite]() ### AI overview This tutorial explains how to use Firebase Machine Learning to enhance TensorFlow Lite model deployment in Android and iOS applications. It covers over-the-air model delivery, downloading models on demand, model management through the Firebase Console or API, and production features such as inference-speed measurement and A/B testing. ### Source excerpt News, tutorials, and updates from the Firebase team. ## ML Kit expands into NLP with Language Identification and Smart Reply DevFeed: [ML Kit expands into NLP with Language Identification and Smart Reply]() Original publisher: [Read original article]() Author: Christiaan Prins; Max Gubin Published: 2019-04-02T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Natural language processing](), [Firebase](), [SDKs](), [Android](), [iOS](), [TensorFlow Lite](), [Transformer]() Tags: [android](), [firebase](), [ios](), [kotlin](), [language](), [launch](), [machine-learning](), [ml-kit](), [news](), [nlp](), [sdk](), [tensorflow-lite](), [updates]() ### AI overview Firebase announces two new ML Kit features: Language Identification and Smart Reply. The APIs support natural language processing, run fully on-device, and are available in the latest ML Kit SDK for iOS and Android. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new at Firebase Summit 2018 DevFeed: [What's new at Firebase Summit 2018]() Original publisher: [Read original article]() Author: Francis Ma Published: 2018-10-29T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Cloud Platform (GCP)](), [Google]() Tags: [a-b-testing](), [app-development](), [app-quality](), [bigquery](), [cloud-functions](), [cloud-messaging](), [cloud-platform](), [community](), [crashlytics](), [data-studio](), [dynamic-audiences](), [firebase](), [firebase-summit](), [firestore](), [google-analytics](), [hosting](), [ios](), [live-streaming](), [machine-learning](), [ml-kit](), [news](), [performance-monitoring](), [predictions](), [realtime-database](), [remote-config](), [summit](), [tensorflow-lite](), [test-lab](), [unity](), [updates](), [web]() ### AI overview Firebase announces updates at Firebase Summit 2018, including beta support for Firebase through Google Cloud Platform support packages. The article also highlights Firebase adoption and a Hotstar case study involving increased user engagement. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new in Firebase at I/O 2018 DevFeed: [What's new in Firebase at I/O 2018]() Original publisher: [Read original article]() Author: Francis Ma Published: 2018-05-08T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [AI Development](), [Mobile](), [SDK](), [Android](), [iOS](), [TensorFlow Lite](), [Amazon Machine Learning]() Tags: [android](), [firebase](), [google-analytics](), [google-cloud](), [google-i-o](), [ios](), [machine-learning](), [ml-kit](), [news](), [on-device](), [performance-monitoring](), [tensorflow-lite](), [test-lab](), [updates]() ### AI overview Firebase announces improvements at I/O 2018, including the public beta of ML Kit, an SDK for adding machine learning features to Android and iOS apps. ML Kit provides ready-to-use APIs for text recognition, face detection, barcode scanning, image labeling, and landmark recognition, with on-device and cloud-based options. Developers can also use their own TensorFlow Lite models, while Firebase handles hosting and serving. The article also begins discussing improvements to Performance Monitoring. ### Source excerpt News, tutorials, and updates from the Firebase team.