ML on GCP: Design-Develop-Deploy

The Machine Learning models developed once with limited resources has to reach multiple clients which should be capable for scaling and learning in real-time. Google's AI Platform makes it easy to take your ML projects from ideation to production and deployment, quickly and cost-effectively.
Learn about ML APIs, AutoML, and Cloud ML Engine products of Google Cloud Platform.

Transcript

1. Hello World!
   

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2. How’s the JOOOSHHH?
   

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3. How’s the JOOOSHHH?
   

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4. ML on GCP:
   Design-Develop-Deploy
   by @CharmiChokshi
   Machine Learning Engineer @Shipmnts.com
   

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5. 90% have failed to solve this...
   

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6. 99.9% have failed to solve this...
   

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7. What we just did?
   

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8. What we just did?
   We solved logical and complex* math puzzles in a fraction of time
   

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9. Think about, How we
   solved them?
   

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10. Think about, How we
    solved them?
    We had inputs
    We also had outputs
    

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11. Think about, How we
    solved them?
    We created Rules and Patterns in our brains!!
    

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12. Can machines create rules
    on their own?
    

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13. Can machines create rules
    on their own?
    No, if it’s Classical Programming...
    

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14. Can machines create rules
    on their own?
    YES, if it’s Machine Learning!!!
    

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15. Classical Programming vs Machine Learning
    Classical
    Programming
    Machine
    Learning
    Rules
    Rules
    Data
    Data
    Answers
    Answers
    

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16. ML Use Cases
    

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17. Why so little ML Apps out there?
    ● Building and Scaling ML infrastructure is hard
    ● Operating production ML system is time consuming and
    Expensive
    

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18. What if, you can get
    ● Fully managed service
    ● Training using custom tensorflow graph for any ML use cases
    ● Training at scale to shorten Dev Cycle
    ● Automatically maximize predictive accuracy with HyperTune
    ● Integrated Datalab experience
    

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19. What if, you can get
    ● Fully managed service
    ● Taring using custom tensorflow graph for any ML use cases
    ● Training at scale to shorten Dev Cycle
    ● Automatically maximize predictive accuracy with HyperTune
    ● Integrated Datalab experience
    At a single place!!!
    

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20. What if, you can get
    ● Fully managed service
    ● Taring using custom tensorflow graph for any ML use cases
    ● Training at scale to shorten Dev Cycle
    ● Automatically maximize predictive accuracy with HyperTune
    ● Integrated Datalab experience
    At a single place!!!
    

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21. ML on Cloud Platform
    

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22. ML on Cloud Platform
    

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23. ML on Cloud Platform
    

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24. ML on Cloud Platform
    

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26. ML on Cloud Platform
    DIY
    

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27. Machine Learning
    as an API
    Access pre-trained models with a single REST API request
    

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28. Available Pre-trained APIs
    

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29. Available Pre-trained APIs
    

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30. Available Pre-trained APIs
    

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32. Let’s identify series characters!
    

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33. Let’s try the Vision API!
    

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34. What if you want to train these
    APIs on custom data?
    

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36. Cloud AutoML
    Train custom models without writing a single line of code
    

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37. The Aha! moment
    

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38. How it works?
    

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39. जीसीपी ही तसरा बाप हे, अहम् ब्रह्माि म!
    

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41. What if you want to
    train/deploy/scale your Keras based
    ML model on GCP?
    Custom task having custom model and data
    

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42. What if your model has
    multiple inputs text and image?
    Can we get predictions for such cases using AutoML?
    

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43. Cloud ML Engine
    Build, Train, and Serve custom models with your own data
    

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44. Ways to create custom models on GCP
    

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45. Some of the GCP offerings
    

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46. Cloud ML Engine: IaaS
    

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47. Where ML Engine fits in ML workflow
    

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48. Components of AI Platform
    ● Training service
    ● Prediction service
    ● Notebooks
    ● Data labeling service (beta)
    ○ Submit a request to label your video, image, or text data with some instructions
    ● Deep learning VM image
    ● Tools to interact with AI Platform
    ○ Google Cloud Console
    ■ Stackdriver Logging
    ■ Stackdriver Monitoring
    ○ REST API
    

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49. Design-Develop-Deploy
    

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50. 3 things
    from this session...
    

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51. ML APIs
    

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52. AutoML
    

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53. Cloud ML Engine
    

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54. Learn more...
    

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55. Questions?
    Comments?
    Suggestions?
    Machine Learning on GCP: Design-Develop-Deploy
    by @CharmiChokshi
    

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56. Thank you!
    Happy Learning :)
    Machine Learning on GCP: Design-Develop-Deploy
    by @CharmiChokshi
    

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