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[DevDay2019] Hands-on Machine Learning on Google Cloud Platform - By Thanh Le, Senior Software Engineer at Upstar Labs

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By recent release on Google Cloud Platform, Google focus on the era of AI/ML technological change, it lets us bring the powerful machine learning features to the mobile application whether it is for Android/iOS and whether experienced/beginner machine learning developer. The purpose of this topic is to share our use case on how to make your model as serving by bringing it to the cloud.

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[DevDay2019] Hands-on Machine Learning on Google Cloud Platform - By Thanh Le, Senior Software Engineer at Upstar Labs

  1. 1. Machine Learning with TensorFlow on Google Cloud Platform DEVDAY 2019 Presented by Thành Quang Lê
  2. 2. I. Introduction 1. What is AI/ML ? 2. Why does it matter ? 3. How Google does it 4. Machine Learning on Google Cloud Platform II. Components 1. TensorFlow Architecture 2. Build A Regression Model 3. Build A Classification Model Agenda III. Q&A
  3. 3. Machine Learning is a specific way of solving AI problems Artificial Intelligence Machine Learning Deep Learning data algorithm predictive insight decision
  4. 4. Deep Learning inspried by structure and function of brain
  5. 5. Easy Quiz? Input: 1, 2, 3, 5, 8, 13, 21, 34, 55 Output: 2, 3, 5, 8, 13, 21, 34, 55, ? Question: What will be the output value for an input value of 55? 89 Fn = Fn-1 + Fn-2F = C * 1.618 + e-7
  6. 6. Another Quiz? Input: 0, 8, 15, 22, 38 Output: 32, 46.4, 59, 71.6, ? Question: What will be the output value for an input value of 38? F = C * 1.8 + 32 100.4
  7. 7. Y = a * X + b
  8. 8. a a If ML ishard,it’s hardforyour competitorstoo aMLis a great differentiator Most MLvalue comes along the way a ML improves almost everything ittouches Enriching our everyday lives
  9. 9. aaa Google apply AI in all their products Classify pictures in GooglePhotos Smart reply inInbox Pedestrian detection Self-drivingcars Recommendations for the next video inYoutube Targeted ads todisplay in Adwords Spam detection inGmail
  10. 10. Usage of AI at Google Used across products: a Directories containing Deep Learning Models
  11. 11. Market Size Prediction Artificial Intelligence as a Service (AIaaS) is expected to reach $77 billion USD in 2025 (growing at a CAGR of 56.7%) By Industry IT & Telecom Retail Energy & Utility Healthcare Others Key Players Alphabet Inc. Apple Inc. Amazon Inc. Facebook Inc. Others
  12. 12. a Compute aa Paradox of choice – Less is more Big Data BigQuery Cloud Dataflow Cloud Dataproc Cloud Datalab Cloud Pub/Sub Genomics Cloud Machine Learning Cloud Vision API Cloud Speech API Cloud Natural Language API Cloud Translation API Cloud Jobs API App Engine Compute Engine Container Engine Container Registry Cloud Functions Machine Learning
  13. 13. AI simple pipeline Cloud Datalab Cloud Storage Events, Metrics and so on Bigtable Cloud ML Engine Applications and Reports Data Studio Dashboards/BI Co-workers Cloud Pub/Sub Cloud Dataflow BigQuery Stream Rawlogs, files, assets, Google Analytics data and so on Batch
  14. 14. a Pre-trained Model Which one should be choosen? Cloud Speech-to-Text Cloud Vision Cloud Text-to-Speech Cloud Translation Cloud Video Intelligence Cloud Natural Language Machine Learning Engine TensorFlow Custom Model
  15. 15. Firebase ML Kit capabilities
  16. 16. TensorFlow Architecture Learn More: TensorFlow 2.0 Alpha Roadmap
  17. 17. Regression Demo
  18. 18. a Regression predicts numeric value Fahrenheit Y X, b a (Celsius) Y = a * X + b F = C * 1.8 + 32
  19. 19. How does DNN model classify the images?
  20. 20. Image classification workflow
  21. 21. Classification Demo
  22. 22. Classification predicts probability Probability of each class Sum of all values == 1 (100%) Input image (28x28 = 784 pixels) Dense layer (128 units) Output (10 units)
  23. 23. QUESTIONS & ANSWER
  24. 24. THANK YOU!
  25. 25. References  https://www.tensorflow.org/  https://firebase.google.com/docs/ml-kit/  https://www.coursera. com/  https://www.udacity.com/  https://machinelearningcoban.com/  https://developers.google.com/protocol-buffers/docs/overview/  https://github.com/grpc-ecosystem/awesome-grpc/  https://code.tutsplus.com/tutorials/rest-vs-grpc-battle-of-the-apis--cms-30711/  https://cloud.google.com/blog/big-data/2016/03/announcing-grpc-alpha-for-google-cloud-pubsub/  https://auth0.com/blog/beating-json-performance-with-protobuf/  https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way- 3bd2b1164a53  https://medium.com/tensorist/classifying-fashion-articles-using-tensorflow-fashion-mnist-f22e8a04728a

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