ML Zero to Hero in GCP

Google Cloud Next Extended Lima 2019

Transcript

1. Machine Learning Zero to Hero in GCP Victoria Ubaldo @Vikyale
2. Victoria Ubaldo Data Analyst Management @Interbank Women Techmakers Lima Ambassador @vikyale
3. ¿Qué vamos a hablar? • Introducción a ML • Cómo empezar con ML • Tecnologías que emplean para ML • ML & Google Cloud • Consejos y tips
4. Introducción a ML
6. La inteligencia artificial (Artificial Intelligence, o AI) es la simulación de procesos de inteligencia humana por parte de máquinas, especialmente sistemas informáticos. Estos procesos incluyen el aprendizaje (la adquisición de información y reglas para el uso de la información), el razonamiento (usando las reglas para llegar a conclusiones aproximadas o definitivas) y la autocorrección.
7. Machine Learning is programming with data.
10. Empowering computer systems with the ability to “learn”.
11. tech sport business politics entertainment
12. DATA: UK mobile owners continue to break records with their text messaging, with latest figures showing that 26 billion texts were sent in total in 2004. ? Prediction Model tech sport business politics entertainment
13. 7 Steps of Machine Learning 1. Gathering Data 2. Preparing that Data 3. Choosing a Model 4. Training 5. Evaluation 6. Hyperparameter Tuning 7. Prediction
15. Machine learning algorithms: - Supervised learning - Unsupervised learning Others: Reinforcement learning, recommender systems.
17. Regresión es predecir un valor, una casa más cercana a un centro comercial y vías principales, regresa un valor en precio más grande, ejemplo 1,000,000 Clasificación es predecir una categoría, se regresa las “clases” o categorías a buscar y en base a eso nos regresa si es “opción 1” o “opción 2”.
19. Cómo empezar con ML
21. GDG Google Cloud ¿Por qué cloud? • Flexibilidad • Pago por uso • Escalabilidad • Alta disponibilidad • Administración • Seguridad • Ubicuidad, localización
22. GDG Google Cloud ¿Por qué cloud? • Flexibilidad • Pago por uso • Escalabilidad • Alta disponibilidad • Administración • Seguridad • Ubicuidad, localización
23. La nube no es más que externalizar tu arquitectura y su administración
24. How Can You Get Started with Machine Learning? Three ways, with varying complexity: (1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.) (2) Use an existing model architecture, and retrain it or fine tune on your dataset (3) Develop your own machine learning models for new problems More flexible, but more effort required
25. Tecnologías a emplear
26. 1. Gathering Data Often overlooked, always undervalued
27. Where does your data live?
28. Gathering data from BigQuery
29. SELECT category, TRIM(LOWER(REGEXP_REPLACE( CONCAT(title, ' ', body), r'["\n\'?,]|<p>|</p>'," " ))) as text FROM `bigquery-public-data.bbc_news.fulltext` ORDER BY RAND() Gathering data from BigQuery
30. Gathering data
31. 2. Preparing that Data Can take a bit longer than expected
32. Our “data” isn’t very organized...
33. coach oscar wednesday oil gadgets animals Building a bag of words model: a simple example Vocabulary Possible labels tech sport business politics entertainment animals measures europe theatre lawyer cats
34. coach oscar wednesday oil gadgets animals Building a bag of words model: a simple example Vocabulary animals electronics europe theatre lawyer cats Inputs gadgets on show at the 2005 consumer electronics show
35. coach oscar wednesday oil gadgets animals Building a bag of words model: a simple example Vocabulary animals electronic s europe theatre lawyer cats Inputs gadgets on show at the 2005 consumer electronics show [ 0 0 0 0 1 0 0 1 0 0 0 0 ]
36. Building a bag of words model: a simple example Labels tech sport business politics entertainment [ 0 1 0 0 0 ]
37. Building a bag of words model: a simple example Labels tech sport business politics entertainment [ 0.01 0.92 0.03 0.02 0.01 ]
38. Building a bag of words model: a simple example Labels tech sport business politics entertainment [ 0.03 0.02 0.04 0.88 0.03 ]
39. 3. Choosing a Model Everyone's favorite conversation topic
41. • Open source Machine Learning library • Especially useful for Deep Learning • For research and production • Apache 2.0 license
42. Hello World Image from https://github.com/mnielsen/neural-networks-and-deep-learning ?
43. What we see What the computer “sees”
44. A multidimensional array. A graph of operations. 3 + 2 5
45. Android iOS ... GPU CPU TensorFlow Distributed Execution Engine ... C++ Frontend Python Frontend
46. Android iOS ... GPU CPU TensorFlow Distributed Execution Engine ... C++ Frontend Python Frontend Layers Build models
47. Android iOS ... GPU CPU TensorFlow Distributed Execution Engine ... C++ Frontend Python Frontend Layers Estimator Keras Model Train and evaluate models Build models
49. 4. Training Surely machine learning is more than this
50. Test and update model Training Data Model Prediction
51. 5. Evaluation Always good to check your work
52. Evaluation Data Prediction Model Calculate accuracy
53. 6. Hyperparameter Tuning Adjusting model parameters
54. Training Data Prediction Model Model Model Model Test and update model
55. 7. Prediction The reason we did all that work
56. DATA: UK mobile owners continue to break records with their text messaging, with latest figures showing that 26 billion texts were sent in total in 2004. Prediction Model tech
57. ML y Google Cloud
58. Cloud Machine Learning Engine No-ops distributed TF training Efficient, parallelized, hyperparameter search Serve TensorFlow, XGBoost, and scikit-learn models in production
59. Cloud Machine Learning APIs See, Hear and Understand the world
60. Cloud Natural Language Cloud Speech Cloud Translate Cloud Vision
61. 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 Cloud Vision API
62. API Usage: Detect Objects in an Image Image Detected Items Vision API Create JSON request with the image or pointer to an image Process the JSON response Call the REST API 1 2 3
63. Confidential & Proprietary Google Cloud Platform 63 Cloud Natural Language API 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
64. Mobile Vision API Providing on-device vision for applications
65. Face API faces, facial landmarks, eyes open, smiling Barcode API 1D and 2D barcodes Text API Latin-based text / structure Common Mobile Vision API Support for fast image and video on-device detection and tracking. NEW!
66. Text Detection Latin based language Understand text structure Photo credit Getty Images
67. Text Structure Blocks Lines Words Lines Words Words Words
68. Barcode Detection 1D barcodes EAN-13/8 UPC-A/E Code-39/93/128 ITF Codabar 2D barcodes QR Code Data Matrix PDF-417 AZTEC UPC DataMatrix QR Code PDF 417 Video and image credit Google
69. Combined Vision & Translation
70. ¿Por dónde empezar?
71. GDG Google Cloud ¡Por dónde empezar! 12 meses 300 $ de crédito gratis para que empieces a utilizar cualquier producto de Google Cloud Platform. Always Free Límites de uso gratuito en los productos que participan en la promoción para los clientes que cumplen los requisitos (durante la prueba gratuita y cuando finalice). La oferta está sujeta a cambios.
72. GDG Google Cloud ¡Por dónde empezar!
73. 7 Steps of Machine Learning 1. Gathering Data 2. Preparing that Data 3. Choosing a Model 4. Training 5. Evaluation 6. Hyperparameter Tuning 7. Prediction
74. What’s Next? Codelabs codelabs.developers.google.com/codelabs/cloud-vision-intro/index.html codelabs.developers.google.com/codelabs/cloud-speech-intro/index.html codelabs.developers.google.com/codelabs/cloud-nl-intro/index.html For Developers cloud.google.com/vision/ cloud.google.com/speech/ cloud.google.com/natural-language/ cloud.google.com/translate/ Stack Overflow
75. Mobile Vision: Codelabs and Samples Googly Eyes Code Sample github.com/googlesamples/android-vision/tree/master/visionSamples/googly-eyes Codelabs codelabs.developers.google.com/codelabs/face-detection/ codelabs.developers.google.com/codelabs/mobile-vision-ocr/ Mobile Vision Developers developers.google.com/vision/ GitHub Code Samples github.com/googlesamples/android-vision Stack Overflow Find and ask questions under the android-vision tag.
76. Thanks! Machine Learning Zero to Hero in GCP Victoria Ubaldo @Vikyale