TensorFlow 2.0 Alpha Seriously Improves the Customer Experience

by Claudia GarciaJul 15, 2019#MachineLearning

TensorFlow is a Machine Learning cross-platform that has started to be adopted widely worldwide. Even though TensorFlow was just released by Google in 2015, it has grown from a software library of machine learning to an entire ecosystem to support different types of Machine Learning, such as deep learning, complex neural networks, AI, just to mention some examples. The TensorFlow 2.0 Alpha is available today. The newest features include the introduction of Keras, a high-level and user-friendly API standard for Machine Learning that simplifies the process of building and training models to be used by beginners and experts. With TensorFlow 2.0, Google is pursuing to improve usability and clarity, at the same time, they had listened to the developers who claimed for more documentation of the library.

During the Google I/O 2019, Google announced the introduction of TensorFlow Lite, a Machine learning framework released for mobile development (Android, iOS, Firebase) using an Apache 2.0 license.

The areas of application for this technology are undoubtedly amazing. Some companies have started to use Tensor Flow to improve the customer experience; eBay, Dropbox, and Airbnb are some examples. On the research field, some of the areas of application of the platform include projects that go from the detection of breast cancer to the forecast of earthquake aftershocks.

If you would like to learn more about TensorFlow 2.0, visit https://medium.com/tensorflow/whats-coming-in-tensorflow-2-0-d3663832e9b8. The TensorFlow 2.0 Beta release is targeted for later this year, but you don’t have to wait, the Alpha release is available today.

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Machine Learning in IoT: Advancements and Applications

The Internet of Things (IoT) is rapidly changing various industries by improving processes and products. With the growth of IoT devices and data transmissions, enterprises are facing challenges in managing, monitoring, and securing devices. Machine learning (ML) can help generate intelligence by working with large datasets from IoT devices. ML can create accurate models that analyze and interpret the data generated by IoT devices, identify and secure devices, detect abnormal behavior, and prevent threats. ML can also authenticate devices and improve user experiences. Other IoT applications benefiting from ML include predictive maintenance, smart homes, supply chain, and energy optimization. Building ML features on IoT edge devices is possible with TensorFlow Lite.