Machine Learning at 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. 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

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[glacier]

13 Smart reply in Inbox by 10% of all responses sent on mobile

14 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 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 .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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