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Google Cloud: Next'19 Extended Hanoi
The document discusses creating a serverless social wall using Firebase and GCP, including features for real-time updates and moderation of social media posts. It also outlines a machine learning flow with GCP, covering various stages from understanding the problem to deploying the model, and highlights the importance of data processing and validation. Key takeaways emphasize reduced developer effort and the scalability of serverless architectures.
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Google Cloud: Next'19 Extended Hanoi
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- 2. ● For today ○Build an application with google assistant and Cloud functions ○ Build a social wall completely Serverless with Firebase and GCP ○ Serverless machine learning at DYNO
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- 4. How to get freelabs? hint: using code
- 5. How to getfree labs? Step 1: Join Qwiklabs
- 6. How to getfree labs? Step 3: Access this link https://go.qwiklabs.com/next-19-extended
- 7. How to getfree labs? Step 2: Buy it…. just kidding!
- 8. How to getfree labs? Step 4: Get started
- 9. How to getfree labs? Step 5: Enter the code
- 10. How to getfree labs? 1t-hanoi-1234
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- 13. Build a YoutubeEntertainment App
- 14. Case Study Building aserverless social wall with Firebase and GCP Lim Shang Yi Software Developer @ IdealHub Google Developer Expert, Firebase
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- 18. Why build asocial wall?
- 19. Why build asocial wall? ● Use for events ● Increase social media presence ● Fun activity before the event starts ● Customize it any way you like
- 20. Features of theSocial Wall ✅ Pull public feeds from Twitter and Instagram, every 2 minutes ✅ New feeds will always appear in front of the list ✅ Moderate posts automatically/manually on monderator screen before showing ✅ Ability to adjust number of lanes on the fly, depending on screen resolution. ✅ Adjust header and subtitle in real time. ✅ Ability to configure hashtag on the fly ✅ Ability to configure how fast/slow new posts appear on screen ✅ Ability to configure how many posts can be queued before showing.
- 21. How would youbuild it?
- 22. How would youbuild it? ● Layouts ● Styles ● Animations ● Pull public feeds from Twitter and Instagram every 2 minutes ● Moderate posts before posting ● Update hashtags, header on the fly ● Update client configurations ● Hosting client app Front end Back end
- 23. How would youbuild it? ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes ● Manage data ● Websocket server ● ... Front end Back endAPI
- 24. ● Store data How wouldyou build it? ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes ● Manage data ● Websocket server ● ... Front end Back end ● REST API ● Websocke ts API DB
- 25. Application Server ● Store data Howwould you build it? ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes ● Manage data ● Websocket server. Front end Back end ● REST API ● Websocke ts API DB
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- 27. Application Server ● Store data Typicalarchitecture ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes ● Manage data ● Websocket server. Front end Back end ● REST API ● Websocke ts API DB
- 28. Application Server ● Store data Serverlessarchitecture with Firebase and GCP ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes ● Manage data ● Websocket server. Front end Back end ● REST API ● Websocke ts API DB
- 29. Firebase ● Store data ●Real-time connection Serverless architecture with Firebase and GCP ● Layouts ● Styles ● Animations ● Pull Twitter/ Instagram every 2 minutes Firebase Hosting Cloud Functions Cloud Firestore ● Trigger Cloud Functions Cloud Scheduler
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- 31. How it works triggersevery 2 minutes Cloud Functions #cat Pull tweets Pull instagram posts Sanitize, and adds social post to Firestore
- 32. How it works Client PullReal-time social posts from Firestore Firebase SDK Administrative Panel Firebase SDK Authenticated Moderate posts/delete social post
- 33. Key takeaways ● Developereffort reduced for key functionalities ● Reliable and scalable performance ● Secure database ● Ability to easily extend services
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- 35. Serverless Machine Learningwith GCP Dao Tuan Vu - vu@dyno.vn CTO DYNO - EWAY JSC
- 36. About Me - CTODYNO - Eway JSC - Experience on backend, database, big data and machine learning
- 37. About Eway - DYNO.vn:Big data services - AdFlex.asia: Top 1 CPA network in Viet Nam - MasOffer.net: Top 1 CPS network in Viet Nam - iHR: Big data for recruiting - Kalapa: Fintech AI - eDoctor.io: Platform for healthcare
- 38. Machine Learning Flow 1.Understanding of business problem 2. Problem formalization 3. Data collecting 4. Data preprocessing 5. Modeling 6. Way to evaluate model in real life 7. Way to deploy model
- 39. Machine Learning Flow 1.Understanding of business problem 2. Problem formalization 3. Data collecting 4. Data preprocessing 5. Modeling 6. Way to evaluate model in real life 7. Way to deploy model
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- 44. Machine Learning Flow 1.Understanding of business problem 2. Problem formalization 3. Data collecting 4. Data preprocessing 5. Modeling 6. Way to evaluate model in real life 7. Way to deploy model
- 45. If you wantto customize model
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- 48. Develop a MachineLearning Model From Scratch?
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- 51. Develop a MachineLearning Model From Scratch?
- 52. AI platform (MLengine)
- 53. Machine Learning Flow 1.Understanding of business problem 2. Problem formalization 3. Data collecting 4. Data preprocessing 5. Modeling 6. Way to evaluate model in real life 7. Way to deploy model
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- 55. Exploratory Data Analysis 1.Cleansing: Checking for problems with the collected data, such as missing data or measurement error, data type of columns, etc.) 2. Defining questions: Identifying relationships between variables that are particularly interesting or unexpected.
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- 62. Machine Learning Flow 1.Understanding of business problem 2. Problem formalization 3. Data collecting 4. Data preprocessing 5. Modeling 6. Way to evaluate model in real life 7. Way to deploy model
- 63. Dataprep 1. Assessing yourdata quality 2. Resolving any issues uncovered 3. Validating big data
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