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Track2 02. machine intelligence at google scale google, kaz sato, staff developer advocate

  1. 1. Machine Intelligence at Google Scale ML APIs, TensorFlow and Cloud ML
  2. 2. +Kazunori Sato @kazunori_279 Kaz Sato Staff Developer Advocate Tech Lead for Data & Analytics Cloud Platform, Google Inc.
  3. 3. What we’ll cover What is Neural Network and Deep Learning Machine Learning use cases at Google services Externalizing the power with ML APIs TensorFlow: the open source library for ML TensorFlow in the Wild Distributed training and prediction with Cloud ML
  4. 4. What is Neural Network and Deep Learning
  5. 5. Neural Network is a function that can learn
  6. 6. xn > b? w1 wn x2 x1 Inspired by the behavior of biological neurons
  7. 7. How do you classify them?
  8. 8. weights bias (threshold) Programmers need to specify the parameters
  9. 9. Let’s see how neural network solves the problem
  10. 10. The computer tries to find the best parameters A neuron classifies a data point into two kinds
  11. 11. Gradient Descent: adjusting the params gradually to reduce errors
  12. 12. How do you classify them?
  13. 13. What we see What the computer “sees”
  14. 14. 28 x 28 gray scale image = 784 numbers
  15. 15. input vector (pixel data) output vector (probability)
  16. 16. How do you classify them?
  17. 17. More neurons = More features to extract
  18. 18. Hidden Layers: mapping inputs to a feature space, classifying with a hyperplane From: Neural Networks, Manifolds, and Topology, colah's blog
  19. 19. From: Neural Networks, Manifolds, and Topology, colah's blog
  20. 20. How about this?
  21. 21. More hidden layers = More hierarchies of features
  22. 22. How about this?
  23. 23. We need to go deeper neural network From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee et al.
  24. 24. From: mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.
  25. 25. How CNN works
  26. 26. Machine Learning use cases at Google services
  27. 27. 27 signal for Search ranking, out 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
  28. 28. WaveNet by DeepMind
  29. 29. 30 [glacier] Google Photos 30
  30. 30. 31 Smart reply in Inbox by Gmail 10% of all responses sent on mobile
  31. 31. Google Translate with Neural Machine Translation
  32. 32. Saved Data Center cooling energy for 40% Improved Power Usage Effectiveness (PUE) for 15%
  33. 33. 34 Android Apps Gmail Maps Photos Speech Search Translation YouTube and many others ... Used across products: 2012 2013 2014 2015 Deep Learning usage at Google
  34. 34. Externalizing the power with ML APIs
  35. 35. TensorFlow Cloud Machine Learning ML API Easy-to-Use, for non-ML engineers Customizable, for Data Scientists Machine Learning products from Google
  36. 36. 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 Vision API
  37. 37. Confidential & ProprietaryGoogle Cloud Platform 38 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
  38. 38. 3939 Demo
  39. 39. Pre-trained models. No ML skill required REST API: receives audio and returns texts Supports 80+ languages Streaming or non-streaming Public Beta - Cloud Speech API
  40. 40. Confidential & ProprietaryGoogle Cloud Platform 41 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
  41. 41. 4242 Demo
  42. 42. Pre-trained models. No ML skill required REST API: receives text and returns analysis results Supports English, Spanish and Japanese GA - Cloud Natural Language API
  43. 43. Confidential & ProprietaryGoogle Cloud Platform 44 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
  44. 44. 4545 Demo
  45. 45. 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 Translation API Premium
  46. 46. 4747 Demo
  47. 47. TensorFlow: An open source library for Machine Intelligence
  48. 48. Google's open source library for machine intelligence launched in Nov 2015 Used by many production ML projects What is TensorFlow?
  49. 49. 50 Sharing our tools with researchers and developers around the world repository for “machine learning” category on GitHub #1 Released in Nov. 2015 From:
  50. 50. 51 Before Hire Data Scientists ↓ Understand the math model ↓ Impl with programming code ↓ Train with single GPU ↓ Build a GPU cluster ↓ Train with the GPU cluster ↓ Build a prediction server or Impl mobile/IoT prediction After Easy network design and impl ↓ Train with single machine ↓ Train on the cloud ↓ Prediction on the cloud or mobile/IoT devices many people stuck here
  51. 51. # 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)
  52. 52. TensorBoard: visualization tool
  53. 53. Portable and Scalable Training on: Mac/Windows GPU server GPU cluster / Cloud Prediction on: Android and iOS RasPi and TPU
  54. 54. Distributed Training with TensorFlow
  55. 55. TensorFlow in the Wild (or democratization of deep learning)
  56. 56. TensorFlow powered Cucumber Sorter From:
  57. 57. TensorFlow powered Cucumber Sorter
  58. 58. TensorFlow powered Fried Chicken Nugget Server From:
  59. 59. TV popstar face generator with DCGAN From:
  60. 60. TensorFlow + Drones for counting trucks From:
  61. 61. TensorFlow+ RasPi for sorting garbages From: trash-sorts-garbage-automatically-at-the-techcrunch- disrupt-hackathon/
  62. 62. Autonomous Driving of RasPi car with Inception 3 on TensorFlow From: Driven-by-Tensorflow
  63. 63. From: Generative Arts with TensorFlow
  64. 64. Distributed Training and Prediction with Cloud ML
  65. 65. From: Andrew Ng The Bigger, The Better
  66. 66. The Challenge: Computing Power DNN requires large training datasets Large models doesn't fit into a GPU Requires try-and-errors to find the best design, configs and params ↓ Need to spend a few days or weeks to finish a training
  67. 67. GPUs run at nanoseconds GPU cluster needs microsec network
  68. 68. Enterprise Google Cloud is The Datacenter as a Computer
  69. 69. Jupiter network 10 GbE x 100 K = 1 Pbps Consolidates servers with microsec latency
  70. 70. Borg No VMs, pure containers 10K - 20K nodes per Cell DC-scale job scheduling CPUs, mem, disks and IO
  71. 71. Distributed Training with TensorFlow by data parallelism split data, share model
  72. 72. CPU/GPU scheduling Communications Local, RPC, RDMA 32/16/8 bit quantization Cost-based optimization Distributed Systems for Large Neural Network
  73. 73. Distributed Training with TensorFlow on Google Cloud "Large Scale Distributed Systems for Training Neural Networks", NIPS 2015 Inception / ImageNet: 40x with 50 GPUs RankBrain: 300x with 500 nodes
  74. 74. Fully managed distributed training and prediction Supports custom TensorFlow graphs HyperTune for hyper-parameter tuning automation Integrated with Cloud Dataflow and Cloud Datalab Public Beta - Cloud Machine Learning (Cloud ML)
  75. 75. Cloud ML at Work: AUCNET The largest real-time car auction service in Japan For 30K used car dealers The auction volume overs $3.7B every year Problem: auction entry is time consuming task for dealers Classifying parts of car for thousands of photos Identifying the exact car model
  76. 76. Solution: Custom Image Classification with TensorFlow/Cloud ML Used 5,000 training images for 500 car models Inception v3 + Transfer Learning Cloud ML: increased training performance for 6x faster
  77. 77. Predicting "large loss" cases in car insurance Old method (Random Forest): 38% accuracy New method (TensorFlow): 73% accuracy A Global Insurance Firm
  78. 78. 8181 Demo
  79. 79. 8282 Ready to use Machine Learning models Use your own data to train models Cloud Vision API Cloud Speech API Cloud Translation API Cloud Machine Learning Develop - Model - Test Google BigQuery Cloud Storage Cloud Datalab Beta GA BetaGA Beta GA GA GA Cloud Natural Language API
  80. 80. Tensor Processing Unit ASIC for TensorFlow Designed by Google 10x better perf / watt latency and efficiency bit quantization
  81. 81. TPU on Production RankBrain AlphaGo Google Photos Speech and more
  82. 82. Thank you!