From Zero to ML on Google Cloud Platform

Everyone’s talking about machine learning, but we hear much less about how to put it into practice. And let’s face it, that can be daunting! Even just 10 years ago you needed access to extensive academic and computing resources to make use of machine learning. Fast forward to today and we’ve seen revolutionary changes in the hardware and software that are making ML accessible for any developer or data scientist. Whether you’re completely new to ML or you’ve already trained and deployed your own model from scratch, Google Cloud Platform has a variety of tools to help you start using ML right now. I’ll start with the basics: how to use a pre-trained ML model with one REST API call. Then you’ll learn how to use your own dataset to customize a pre-trained model with transfer learning. We’ll end by learning how to build your own model from scratch with TensorFlow, and how train and serve it in the cloud with GCP.

Transcript

1. Confidential & Proprietary Zero to ML on GCP Sara Robinson Developer Advocate @SRobTweets
2. Confidential & Proprietary What is machine learning? @SRobTweets
3. Confidential & Proprietary Learning from examples and experience @SRobTweets
4. Confidential & Proprietary How did you learn your first language? @SRobTweets
5. Confidential & Proprietary ML is... Solving problems without explicitly knowing the solution Loosely based on how the human brain learns Enable systems that improve over time @SRobTweets
6. Confidential & Proprietary inputs, labels Model predictions Framing a machine learning problem @SRobTweets
7. Confidential & Proprietary How do we get from input to prediction? @SRobTweets
8. Confidential & Proprietary How do we get from input to prediction? “cat” @SRobTweets
9. Confidential & Proprietary How do we get from input to prediction? “cat” @SRobTweets
10. Confidential & Proprietary How do we get from input to prediction? @SRobTweets
11. Confidential & Proprietary How do we get from input to prediction? sports @SRobTweets
12. Confidential & Proprietary How do we get from input to prediction? sports baseball @SRobTweets
13. Confidential & Proprietary How do we get from input to prediction? sports baseball nytimes @SRobTweets
14. Confidential & Proprietary How do we get from input to prediction? @SRobTweets
15. Confidential & Proprietary How do we get from input to prediction? mountain pass nature birthday cake family @SRobTweets
16. Confidential & Proprietary Is machine learning only for experts?
17. Confidential & Proprietary First neural network 1957 @SRobTweets
18. Confidential & Proprietary First neural network 1957
19. Confidential & Proprietary
20. Confidential & Proprietary Democratizing ML @SRobTweets
21. Confidential & Proprietary What type of ML problem are you solving? Someone else has solved this before Specific to your dataset Custom task Generic task @SRobTweets
22. Confidential & Proprietary Custom task Generic task What type of ML problem are you solving? Someone else has solved this before Specific to your dataset “cat” “bob” Example: image classification @SRobTweets
23. Confidential & Proprietary Custom task Generic task What type of ML problem are you solving? Someone else has solved this before Specific to your dataset verb noun pronoun programming Google Cloud Example: NLP @SRobTweets
24. Confidential & Proprietary From zero… Machine Learning APIs ...to ML AutoML Custom model App developers Data scientists & ML practitioners @SRobTweets
25. Confidential & Proprietary What resources do you need to solve an ML problem? Training data Model code Training + serving infrastructure Prediction code Time Custom model: build from scratch Custom model: transfer learning AutoML ML APIs @SRobTweets
26. ML as an API Use a pre-trained model with a single REST API request
27. Confidential & Proprietary Use a pre-trained model to accomplish common ML tasks Cloud Vision Cloud Translation Cloud Natural Language Cloud Speech Cloud Video Intelligence @SRobTweets
28. Confidential & Proprietary Let’s look at Cloud Video & NL Cloud Vision Cloud Translation Cloud Natural Language Cloud Speech Cloud Video Intelligence @SRobTweets
29. Confidential & Proprietary Cloud Vision @SRobTweets
30. Confidential & Proprietary Label & web detection OCR Logo detection Explicit content detection Crop hints Landmark detection
31. Confidential & Proprietary Cloud Vision in production: GIPHY For more insight visit: Engineering.giphy.com @GIPHYEng
32. Confidential & Proprietary Let’s see a demo! @SRobTweets
33. Confidential & Proprietary Calling the Vision API from Node.js const gcloud = require('google-cloud'); const vision = gcloud.vision(); const types = [ 'face', 'label' ]; vision.detect('image.jpg', types, function(err, detections, apiResponse) { // detections = { // faces: [...], // labels: [...] // } }); @SRobTweets
34. Confidential & Proprietary Cloud Natural Language @SRobTweets
35. Extract entities Detect sentiment Analyze syntax Classify content
36. Confidential & Proprietary Analyze syntax The natural language API helps us understand text . Dependency Parse label det nn nn nsubj root nsubj ccomp dobj p Part of speech DET NOUN NOUN NOUN VERB PRON VERB NOUN PUNCT Lemma help Number = SINGULAR Proper = PROPER Number = SINGULAR Proper = PROPER Number = SINGULAR Proper = PROPER Mood = INDICATIVE Number = SINGULAR Person = THIRD Tense = Present Case = ACCUSATIVE Number = PLURAL Person = FIRST Number = SINGULAR Morphology @SRobTweets
37. Confidential & Proprietary Classify Content Rafael Montero Shines in Mets’ Victory Over the Reds. Montero, who was demoted at midseason, took a one-hitter into the ninth inning as the Mets continued to dominate Cincinnati with a win at Great American Ball Park. { categories: [ { name: '/Sports/Team Sports/Baseball', confidence: 0.99 } ] } @SRobTweets
38. Confidential & Proprietary Let’s see a demo! @SRobTweets
39. Confidential & Proprietary Calling the NL API from Node.js const gcloud = require('google-cloud'); const language = gcloud.language(); const bucket = gcs.bucket('my-bucket'); const file = bucket.file('my-file'); function callback(err, entities, apiResponse) {} language.annotate(file, callback); @SRobTweets
40. Confidential & Proprietary Use a pre-trained model to accomplish common ML tasks Cloud Vision Cloud Translation Cloud Natural Language Cloud Speech Cloud Video Intelligence @SRobTweets
41. Confidential & Proprietary What if we want to train these APIs on our own custom data? @SRobTweets
42. AutoML **alpha Use your own data to customize a pre-trained API
43. Confidential & Proprietary Let’s say I’m a meteorologist
44. Confidential & Proprietary I want to predict weather trends and flight plans from images
45. Confidential & Proprietary Can we use the cloud to analyze clouds?
46. Confidential & Proprietary There are 10+ different types of clouds
47. Confidential & Proprietary They all indicate different weather patterns
48. Confidential & Proprietary Let’s try the Vision API @SRobTweets
49. Confidential & Proprietary AutoML Vision to the rescue AutoML Vision Photo dataset Train Deploy Serve Generate predictions with a REST API @SRobTweets
50. Confidential & Proprietary Who is using AutoML Vision? In the past, ZSL used to track animals and understand their lives by having a human review photos from cameras deployed in the wild. Now they can tag these pictures automatically, enabling a deeper understanding across wider geographies to help protect the world’s wildlife. Build vision models to annotate shop Disney’s products with Disney characters, product category, and dominant color. Annotations are being integrated into Disney’s search engine to help users get more relevant results and find their ideal products faster. Building a model to create a comprehensive set of product attributes to improve product recommendations, search results, and product filters. Recognizing nuanced characteristics like patterns and neckline styles. @SRobTweets
51. Confidential & Proprietary What if you have a custom prediction task specific to your dataset or use case? @SRobTweets
52. Confidential & Proprietary Custom text classification @SRobTweets
53. Confidential & Proprietary Custom text classification javascript c++ sql @SRobTweets
54. Confidential & Proprietary Given demographic data, can we predict voting trends? @SRobTweets
55. Confidential & Proprietary Given demographic data, can we predict voting trends? inputs prediction @SRobTweets
56. Confidential & Proprietary Identifying the location of objects in an image @SRobTweets
57. Confidential & Proprietary Identifying the location of objects in an image sara 97% Kitten 99% @SRobTweets
58. Roll your own Build and train a custom model with your own data
59. Confidential & Proprietary Build train and serve your own models TensorFlow ML Engine @SRobTweets
60. Confidential & Proprietary Build train and serve your own models TensorFlow ML Engine @SRobTweets
61. Confidential & Proprietary TensorFlow Created by Google Brain team Most popular ML project on GitHub Multiple deployment options @SRobTweets
62. Confidential & Proprietary TensorFlow Distributed Execution Engine CPU GPU Android iOS ... C++ Frontend Python Frontend ... @SRobTweets
63. Confidential & Proprietary TensorFlow Distributed Execution Engine CPU GPU Android iOS ... C++ Frontend Python Frontend ... tf.layers Build models @SRobTweets
64. Confidential & Proprietary TensorFlow Distributed Execution Engine CPU GPU Android iOS ... C++ Frontend Python Frontend ... Estimator tf.layers Build models Keras @SRobTweets
65. Confidential & Proprietary TensorFlow Distributed Execution Engine CPU GPU Android iOS ... C++ Frontend Python Frontend ... Models in a box Estimator tf.layers Build models Keras Pre-built Estimators @SRobTweets
66. Confidential & Proprietary TensorFlow Distributed Execution Engine CPU GPU Android iOS ... C++ Frontend Python Frontend ... Layers Pre-built Estimators Estimator Keras @SRobTweets
67. Confidential & Proprietary Once you’ve got a TensorFlow model, what about…. Training Serving & @SRobTweets
68. Confidential & Proprietary Cloud Machine Learning Engine (ML Engine) Fully managed platform for TensorFlow Distributed Training with GPUs (and TPUs) Fast and scalable online/batch prediction cloud.google.com/ml-engine @SRobTweets
69. Confidential & Proprietary ML Engine in 3 steps Prepare your TensorFlow code and training data on Cloud Storage Run CMLE job with gcloud command Monitor the job on Cloud Console @SRobTweets
70. Confidential & Proprietary Example: building a Taylor Swift detector @SRobTweets
71. Confidential & Proprietary TensorFlow Object Detection to build the object recognition model @SRobTweets
72. Confidential & Proprietary ...and ML Engine to train, serve, and generate predictions @SRobTweets
73. Confidential & Proprietary TensorFlow Object Detection API @SRobTweets
74. Confidential & Proprietary TensorFlow Object Detection API @SRobTweets
75. Confidential & Proprietary Need a custom model but don’t have enough training data? Transfer learning Model trained on lots of data Your data Updated output using your training data @SRobTweets
76. Confidential & Proprietary How does it all fit together? Learn more: bit.ly/swift-detector @SRobTweets
77. Confidential & Proprietary What if we have a custom task and enough training data to build + train our model from scratch? @SRobTweets
78. Confidential & Proprietary Custom model: build from scratch Training data Model code Serving infrastructure Prediction code Time @SRobTweets
79. Confidential & Proprietary There are ~3,000 counties in the US Can we use demographic data to predict which way a county will vote? Example: a classification model to predict voting trends @SRobTweets
80. Confidential & Proprietary Get the data → Kaggle @SRobTweets
81. Confidential & Proprietary Select features from BigQuery @SRobTweets
82. Confidential & Proprietary From this... inputs prediction @SRobTweets
83. Confidential & Proprietary ...to this inputs prediction @SRobTweets
84. Confidential & Proprietary Preprocess our input data import pandas as pd df = pd.read_csv('data.csv', header=None) for idx, row in df.iterrows(): # Convert % to binary classes: 0 = Trump, 1 = Clinton classification_val = 0 if row[7] >= .50: classification_val = 1 classification_row = row.as_matrix() classification_row[7] = classification_val new_rows.append(classification_row) # Write new_rows to new csv file @SRobTweets
85. Confidential & Proprietary Define feature columns feature_names = [ 'PctUnder18', 'PctOver65', 'PctFemale', 'PctWhite', 'PctBachelors', 'PctDem', 'PctGop' ] feature_columns = [tf.feature_column.numeric_column(k) for k in feature_names] @SRobTweets
86. Confidential & Proprietary Define our input function def my_input_fn(file_path, repeat_count): def decode_csv(line): features = tf.decode_csv(line, [[0.],[0.],[0.],[0.],[0.],[0.],[0.],[0.]]) label = features[-1:] del features[-1] return dict(zip(feature_names, features)), label dataset = (tf.data.TextLineDataset(file_path) .map(decode_csv) .shuffle(buffer_size=256) .repeat(repeat_count) .batch(8)) iterator = dataset.make_one_shot_iterator() batch_features, batch_labels = iterator.get_next() return batch_features, batch_labels @SRobTweets
87. Confidential & Proprietary Define our input function def my_input_fn(file_path, repeat_count): def decode_csv(line): features = tf.decode_csv(line, [[0.],[0.],[0.],[0.],[0.],[0.],[0.],[0.]]) label = features[-1:] del features[-1] return dict(zip(feature_names, features)), label dataset = (tf.data.TextLineDataset(file_path) .map(decode_csv) .shuffle(buffer_size=256) .repeat(repeat_count) .batch(8)) iterator = dataset.make_one_shot_iterator() batch_features, batch_labels = iterator.get_next() return batch_features, batch_labels @SRobTweets
88. Confidential & Proprietary Define our input function def my_input_fn(file_path, repeat_count): def decode_csv(line): features = tf.decode_csv(line, [[0.],[0.],[0.],[0.],[0.],[0.],[0.],[0.]]) label = features[-1:] del features[-1] return dict(zip(feature_names, features)), label dataset = (tf.data.TextLineDataset(file_path) .map(decode_csv) .shuffle(buffer_size=256) .repeat(repeat_count) .batch(16)) iterator = dataset.make_one_shot_iterator() batch_features, batch_labels = iterator.get_next() return batch_features, batch_labels @SRobTweets
89. Confidential & Proprietary Define our input function def my_input_fn(file_path, repeat_count): def decode_csv(line): features = tf.decode_csv(line, [[0.],[0.],[0.],[0.],[0.],[0.],[0.],[0.]]) label = features[-1:] del features[-1] return dict(zip(feature_names, features)), label dataset = (tf.data.TextLineDataset(file_path) .map(decode_csv) .shuffle(buffer_size=256) .repeat(repeat_count) .batch(16)) iterator = dataset.make_one_shot_iterator() batch_features, batch_labels = iterator.get_next() return batch_features, batch_labels @SRobTweets
90. Confidential & Proprietary Create a classification model classifier = tf.estimator.LinearClassifier( feature_columns=feature_columns ) @SRobTweets
91. Confidential & Proprietary Run training & evaluation # Run training for 10 epochs classifier.train( input_fn=lambda: my_input_fn(train_file, 10)) # Evaluate the accuracy of our trained model results = classifier.evaluate( input_fn=lambda: my_input_fn(test_file, 1)) @SRobTweets
92. Confidential & Proprietary Run training & evaluation # Evaluation output accuracy: 0.965686 auc_precision_recall: 0.942694 average_loss: 0.110322 label/mean: 0.111111 prediction/mean: 0.130689 @SRobTweets
93. Confidential & Proprietary Generate predictions predict_results = classifier.predict( input_fn=lambda: my_input_fn(test_file, 4) ) @SRobTweets
94. Confidential & Proprietary Generate predictions { 'probabilities': array( [ 0.99163872, 0.00836132], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object) } @SRobTweets
95. Confidential & Proprietary Generate predictions { 'probabilities': array( [ 0.99163872, 0.00836132], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object) } @SRobTweets
96. Confidential & Proprietary Let’s see it in action! @SRobTweets
97. Confidential & Proprietary If you remember 3 things from this presentation
98. Confidential & Proprietary Use a pre-trained API to accomplish common ML tasks like image analysis, NLP, or translation @SRobTweets
99. Confidential & Proprietary To build an image classification API trained on your own data, use AutoML Vision @SRobTweets
100. Confidential & Proprietary For custom tasks, build a TensorFlow model with your data. Train and serve it on ML Engine. @SRobTweets
101. Confidential & Proprietary Video: cloud.google.com/video-intelligence Vision: cloud.google.com/vision Speech: cloud.google.com/speech Natural Language: cloud.google.com/natural-language API overview talk: bit.ly/ml-apis-overview Sign up for the alpha: cloud.google.com/automl Announcement blog post: bit.ly/announcing-automl Intro video: bit.ly/automl-intro-video Podcast: bit.ly/automl-podcast Object Detection: bit.ly/tf-obj-detection T-Swift detector blog: bit.ly/swift-detector Pet detector blog: bit.ly/pet-model-ml-engine TensorFlow: tensorflow.org ML Engine: cloud.google.com/ml-engine TF Estimators: bit.ly/tf-estimators Keras blog post: bit.ly/text-classification-keras ML APIs AutoML Transfer learning Custom model @SRobTweets
102. Confidential & Proprietary Thank you @SRobTweets medium.com/@SRobTweets