TensorFlow is nowadays the primary software tool of deep learning. It is an open-source artificial intelligence library that applies data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation.
The team's task was to create a fast and effective way to prototype, train and release machine learning models for the speech-processing framework
We've developed a fast and effective approach that quickly turns a prototype into a production-ready machine learning component. One of the main advantages of this approach is that most of the code written at the first stage can be transfered to further stages. There is no need to do codegeneration or translation to other languages, frameworks etc. Moreover, in this way you have access to multiple versions of your version and can easily switch between them.
Firstly, a prototype is built on a local machine using Tensorflow with CPU and smaller-size models. It is good practice to build a Tensorflow from sources because it doubles the speed of CPU calculations. As soon as the pipeline is debugged, the data collection code is ready and a simple baseline is estimated, we transfer training to a bigger GPU machine and switch to a more complex model (AWS p2.xlarge fits perfectly). When the desired accuracy is reached, the model is packed and and uploaded to the models repository using artifactory.
For Tensorflow, it's recommended to reuse most of the training code in the model serving module. If you are OK with using gRPC for service communication, then Tensorflow Serving is a powerful tool.
Probabilistic graphical models
unsupervised sound segmentation
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