Giovanni Galloro - Make your applications see, understand and talk with Google Cloud AI - Codemotion Rome 2019
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We will explore some of the AI capabilities present on GCP that are making Machine Learning accessible to any developer: from recognizing what’s inside an image to understand what your users are saying and how they feel about it and answering their questions.
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Giovanni Galloro - Make your applications see, understand and talk with Google Cloud AI - Codemotion Rome 2019
- 1. Giovanni Galloro - Customer Engineer, Google Cloud @ggalloro Rome | March 22 - 23, 2019 Make your applications see, understand and talk with Google Cloud AI
- 2. 1 Machine Learning @ Google
- 3. Fast growth in ML adoption at Google Google projects containing ML Models 2012 2013 2014 2015 3000 2000 1000 0 Used across products: 4000 2016 Uniqueprojectdirectories Time
- 4. An open source solution Created by Google Brain team Most popular ML project on Github ● Over 480 contributors ● 10,000 commits in 12 months Multiple deployment options: ● Mobile, Desktop, Server, Cloud ● CPU, GPU
- 5. 2 AI Capabilities on Google Cloud Platform
- 6. Matching the ML tool to the job AutoML Some training data required. Little/no ML expertise required. Custom TensorFlow model Training and ML expertise required Machine Learning APIs Use pre-trained models. No training data or ML expertise required.
- 7. Functionality of Cloud AI building blocks Conversation Cloud Speech-to-Text Dialogflow Enterprise Edition Cloud Text-to-Speech Sight Vision Video Intelligence AutoML Vision Language Translation Natural Language AutoML Translation AutoML Natural Language
- 8. 3 Build Apps that Can See
- 9. Cloud Vision API Label & web detection OCR Logo detection Explicit content detectionCrop hintsLandmark detection
- 10. AutoML Vision Let’s say I’m a meteorologist
- 11. I want to predict weather trends and flight plans from images of clouds AutoML Vision
- 12. There are many different types of clouds AutoML Vision
- 13. They all indicate different weather patterns AutoML Vision
- 14. Let’s try the Vision API
- 15. AutoMLDataset Train Deploy Serve Generate predictions with a REST API AutoML ML that creates ML for your problem
- 16. 4 Build Apps that Can Listen, Understand and Talk
- 17. Dialogflow - Hybrid ML powered intent matching - Automated entity recognition - Dialog-based analytics
- 19. Helpdesk Chatbot Example “Ok Google, talk to Helpdesk Support” Invoke “Helpdesk support” action “Hi! Welcome to Helpdesk...” Speech to Text “No Problem! Please provide me your username..” “I want to submit a ticket” Text to Speech “Sure, here’s Heldesk Support” Speech to Text, NLP, Knowledge Graph, ML Ranking, User Profile Text to Speech
- 20. Thank You