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!
2. How’s the JOOOSHHH?
3. How’s the JOOOSHHH?
4. ML on GCP: Design-Develop-Deploy by @CharmiChokshi Machine Learning Engineer @Shipmnts.com
5. 90% have failed to solve this...
6. 99.9% have failed to solve this...
7. What we just did?
8. What we just did? We solved logical and complex* math puzzles in a fraction of time
9. Think about, How we solved them?
10. Think about, How we solved them? We had inputs We also had outputs
11. Think about, How we solved them? We created Rules and Patterns in our brains!!
12. Can machines create rules on their own?
13. Can machines create rules on their own? No, if it’s Classical Programming...
14. Can machines create rules on their own? YES, if it’s Machine Learning!!!
15. Classical Programming vs Machine Learning Classical Programming Machine Learning Rules Rules Data Data Answers Answers
16. ML Use Cases
17. Why so little ML Apps out there? • Building and Scaling ML infrastructure is hard • Operating production ML system is time consuming and Expensive
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
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!!!
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!!!
21. ML on Cloud Platform
22. ML on Cloud Platform
23. ML on Cloud Platform
24. ML on Cloud Platform
26. ML on Cloud Platform DIY
27. Machine Learning as an API Access pre-trained models with a single REST API request
28. Available Pre-trained APIs
29. Available Pre-trained APIs
30. Available Pre-trained APIs
32. Let’s identify series characters!
33. Let’s try the Vision API!
34. What if you want to train these APIs on custom data?
36. Cloud AutoML Train custom models without writing a single line of code
37. The Aha! moment
38. How it works?
39. जीसीपी ही तसरा बाप हे, अहम् ब्रह्माि म!
41. What if you want to train/deploy/scale your Keras based ML model on GCP? Custom task having custom model and data
42. What if your model has multiple inputs text and image? Can we get predictions for such cases using AutoML?
43. Cloud ML Engine Build, Train, and Serve custom models with your own data
44. Ways to create custom models on GCP
45. Some of the GCP offerings
46. Cloud ML Engine: IaaS
47. Where ML Engine fits in ML workflow
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
49. Design-Develop-Deploy
50. 3 things from this session...
51. ML APIs
52. AutoML
53. Cloud ML Engine
54. Learn more...
55. Questions? Comments? Suggestions? Machine Learning on GCP: Design-Develop-Deploy by @CharmiChokshi
56. Thank you! Happy Learning :) Machine Learning on GCP: Design-Develop-Deploy by @CharmiChokshi