Exciting news from Google I/O! The company has just launched PaLM 2, a large language model, but that’s not all. Google is also introducing a series of open-source machine learning (ML) technology updates and enhancements for the growing TensorFlow ecosystem. TensorFlow is an open-source technology effort that provides ML tools to help developers build and train models.
Google is also launching its new DTensor technology at Google I/O, which brings new parallelism techniques to ML training, helping to improve model training and scaling efficiency.
But that’s not all! Google is also releasing a preview of the TF Quantization API, which is intended to help make models more resource-efficient overall and thus reduce the cost of development.
On Tuesday, Google announced its latest effort in making machine learning development faster and more accessible with the release of TensorFlow 2.1, its open-source machine learning library.Google’s TensorFlow, first released in 2015, is a suite of open-source libraries used for artificial intelligence (AI) and machine learning. It has since become one of the most popular machine learning frameworks in use today, being adopted by major technology companies such as AMD, ARM and Intel.
In the latest iteration, Google has added several new features to TensorFlow. These include an improved API for defining high-level linear algebra operations, easier deployment of deployable models, enhanced internationalization support and the introduction of Eager Execution.
The Eager Execution feature provides a more intuitive paradigm for writing machine learning applications. Instead of constructing complex graphs and dataflow pipelines, developers can now write intuitive code akin to that used in a traditional Python program. This can significantly reduce development efforts and accelerates the development of increasingly sophisticated machine learning programs.
Another major improvement is the introduction of the TensorFlow Extended platform. This platform adds a host of additional features to the TensorFlow ecosystem, including support for a range of popular data science and machine learning frameworks. This allows users to rapidly build production-level applications, while leveraging the existing TensorFlow primitives.
Google has also added support for accelerated computing with the Tensor Processing Unit (TPU). This allows users to take advantage of Google’s custom-built silicon chips, providing near-instantaneous compute capabilities in the cloud.
With these new features, Google hopes to further enhance the capabilities of TensorFlow, making it easier and faster for developers to build ever more powerful machine learning applications. The new version of TensorFlow is available now and is free to use.
Overall, Google’s latest expansion of TensorFlow open-source tools is a major step forward for machine learning development. By making it easier and faster to develop powerful applications, Google is helping to build the future of machine intelligence.
