Machine Learning at Google Scale ML APIs and TensorFlow Michel - - PowerPoint PPT Presentation

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

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signal

for Search ranking,

• ut of hundreds

improvement

to ranking quality in 2+ years

#3 #1

Search

machine learning for search engines

RankBrain: a deep neural network for search ranking

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[glacier]

Google Photos

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14

Smart reply in Inbox by Gmail

10%

• f all responses

sent on mobile

Google Translate with Neural Machine Translation

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Android Apps Gmail Maps Photos Speech Search Translation YouTube and many others ...

Used across products:

2012 2013 2014 2015

Deep Learning usage at Google

Externalizing the power with ML APIs

TensorFlow Cloud Machine Learning ML API

Easy-to-Use, for non-ML engineers Customizable, for Data Scientists

Machine Learning products from Google

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

Cloud Vision API

Faces Faces, facial landmarks, emotions OCR Read and extract text, with support for > 10 languages Label Detect entities from furniture to transportation Logos Identify product logos Landmarks & Image Properties Detect landmarks & dominant color of image Safe Search Detect explicit content - adult, violent, medical and spoof

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Demo

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

Cloud Speech API

Features

Automatic Speech Recognition (ASR) powered by deep learning neural networking to power your applications like voice search or speech transcription. Recognizes over 80 languages and variants with an extensive vocabulary. Returns partial recognition results immediately, as they become available. Filter inappropriate content in text results. Audio input can be captured by an application’s microphone or sent from a pre-recorded audio

• file. Multiple audio file formats are supported,

including FLAC, AMR, PCMU and linear-16. Handles noisy audio from many environments without requiring additional noise cancellation. Audio files can be uploaded in the request and, in future releases, integrated with Google Cloud Storage.

Automatic Speech Recognition Global Vocabulary Inappropriate Content Filtering Streaming Recognition Real-time or Buffered Audio Support Noisy Audio Handling Integrated API

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Demo

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

Cloud Natural Language API

Features

Extract sentence, identify parts of speech and create dependency parse trees for each sentence. Identify entities and label by types such as person, organization, location, events, products and media. Understand the overall sentiment of a block of text.

Syntax Analysis Entity Recognition Sentiment Analysis

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Demo

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

Cloud Translation API Premium

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Demo

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

Cloud Video Intelligence API

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Demo

TensorFlow: An open source library for Machine Intelligence

Google's open source library for machine intelligence tensorflow.org launched in Nov 2015 Used by many production ML projects

What is TensorFlow?

# 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

repository

for “machine learning” category on GitHub

#1

Released in Nov. 2015

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

Enterprise

Google Cloud is

The Datacenter as a Computer

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Thank you!