Machine Learning at Google Scale ML APIs and TensorFlow Michel Pereira
Google Cloud Customer Engineer
@michelpereira@ What is Neural Network and Deep Learning Neural Network is a function that can learn
How about this? More hidden layers = More hierarchies of features How about this?
We need to go deeper neural network
From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee et al. Machine Learning use cases at Google services Search machine learning for search engines
RankBrain: a deep neural network for search ranking
#3 #1 signal improvement for Search ranking, to ranking quality out of hundreds in 2+ years
[glacier]
13 Smart reply in Inbox by Gmail 10% of all responses sent on mobile
14 Google Translate with Neural Machine Translation Deep Learning usage at Google
Used across products: Android Apps Gmail Maps Photos Speech Search Translation YouTube
2012 2013 2014 2015 and many others ...
16 Externalizing the power with ML APIs Machine Learning products from Google
Easy-to-Use, for non-ML engineers
TensorFlow Cloud Machine Learning ML API
Customizable, for Data Scientists Cloud Vision API
Image analysis with pre-trained models
No Machine Learning skill required
REST API: receives an image and returns a JSON
$1.50 per 1,000 units
GA - cloud.google.com/vision Label OCR Faces Detect entities from furniture to Read and extract text, with Faces, facial landmarks, emotions transportation support for > 10 languages
Safe Search Landmarks & Image Properties Logos Detect explicit content - adult, Detect landmarks & dominant Identify product logos violent, medical and spoof color of image
Google Cloud Platform Confidential & Proprietary 20 Demo
21 Cloud Speech API
Pre-trained models. No ML skill required
REST API: receives audio and returns texts
Supports 80+ languages
Streaming or non-streaming
Public Beta - cloud.google.com/speech Features
Automatic Speech Recognition Global Vocabulary Streaming Recognition Inappropriate Content Filtering Automatic Speech Recognition (ASR) Recognizes over 80 Returns partial Filter inappropriate powered by deep learning neural languages and variants recognition results content in text results. networking to power your with an extensive immediately, as they applications like voice search or vocabulary. become available. speech transcription.
Real-time or Buffered Audio Support Noisy Audio Handling Integrated API
Handles noisy audio from many Audio files can be uploaded in the Audio input can be captured by an application’s environments without requiring request and, in future releases, microphone or sent from a pre-recorded audio additional noise cancellation. integrated with Google Cloud file. Multiple audio file formats are supported, Storage. including FLAC, AMR, PCMU and linear-16.
Google Cloud Platform Confidential & Proprietary 23 Demo
24 Cloud Natural Language API
Pre-trained models. No ML skill required
REST API: receives text and returns analysis results
Supports English, Spanish and Japanese
GA - cloud.google.com/natural-language Features
Syntax Analysis Entity Recognition
Extract sentence, identify parts of Identify entities and label by types such speech and create dependency parse as person, organization, location, events, trees for each sentence. products and media.
Sentiment Analysis
Understand the overall sentiment of a block of text.
Google Cloud Platform Confidential & Proprietary 26 Demo
27 Cloud Translation API Premium
Pre-trained models. No ML skill required
REST API: receives text and returns translated text
8 languages: English to Chinese, French, German, Japanese, Korean, Portuguese, Spanish, Turkish Public Beta - cloud.google.com/translate Demo
29 Cloud Video Intelligence API
Video analysis with pre-trained models
No Machine Learning skill required
REST API: receives a video and returns a JSON
Private Beta - cloud.google.com/video-intelligence Demo
31 TensorFlow: An open source library for Machine Intelligence What is TensorFlow?
Google's open source library for machine intelligence tensorflow.org launched in Nov 2015 Used by many production ML projects # define the network import tensorflow as tf x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b)
# define a training step y_ = tf.placeholder(tf.float32, [None, 10]) xent = -tf.reduce_sum(y_*tf.log(y)) step = tf.train.GradientDescentOptimizer(0.01).minimize(xent) TensorBoard: visualization tool Portable and Scalable
Training on:
Mac/Windows
GPU server
GPU cluster / Cloud
Prediction on:
Android and iOS
RasPi and TPU Sharing our tools with researchers and developers around the world
Released in Nov. 2015
#1 repository for “machine learning” category on GitHub
From: http://deliprao.com/archives/168 TensorFlow community and ecosystem From: https://www.qualcomm.com/news/snapdragon/2017/01/09/tensorflow-machine-learning-now-optimized-snapdragon-835-and-hexagon-682
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