TensorFlow 2.0 and Cloud AI make it easy to train, deploy, and maintain scalable machine learning models

Experiment and iterate

Both researchers and enterprise data science teams must continuously iterate on model architectures, with a focus on rapid prototyping and speed to a first solution. With eager execution a focus in TensorFlow 2.0, researchers have the ability to use intuitive Python control flows, optimize their eager code with tf.function, and save time with improved error messaging. Creating and experimenting with models using TensorFlow has never been so easy.

Faster training is essential for model deployments, retraining, and experimentation. In the past year, the TensorFlow team has worked diligently to improve training performance times on a variety of platforms including the second-generation Cloud TPU (by a factor of 1.6x) and the NVIDIA V100 GPU (by a factor of more than 2x). For inference, we saw speedups of over 3x with Intel’s MKL library, which supports CPU-based Compute Engine instances.

Through add-on extensions, TensorFlow expands to help you build advance models. For example, TensorFlow Federated lets you train models both in the cloud and on remote (IoT or embedded) devices in a collaborative fashion. Often times, your remote devices have data to train on that your centralized training system may not. We also recently announced the TensorFlow Privacy extension, which helps you strip personally identifiable information (PII) from your training data. Finally, TensorFlow Probability extends TensorFlow’s abilities to more traditional statistical use cases, which you can use in conjunction with other functionality like estimators.

Deploy your ML model in a variety ofenvironments and languages

A core strength of TensorFlow has always been the ability to deploy models into production. In TensorFlow 2.0, the TensorFlow team is making it even easier. TFX Pipelines give you the ability to coordinate how you serve your trained models for inference at runtime, whether on a single instance, or across an entire cluster. Meanwhile, for more resource-constrained systems, like mobile or IoT devices and embedded hardware, you can easily quantize your models to run with TensorFlow Lite. Airbnb, Shazam, and the BBC are all using TensorFlow Lite to enhance their mobile experiences, and to validate as well as classify user-uploaded content.