From Zero to ML on Google Cloud Platform

A quick start into your machine learning journey on Google Cloud Platform

Transcript

1. From Zero to ML on Google Cloud Platform +OlayinkaPeterOluwafemi @olayinkapeter_ Organizer, Google Cloud Developer Community, Ado-Ekiti, Nigeria GDG DevFest Makurdi 2018
2. What is machine learning?
3. Generic task Custom task Stuff someone has solved before Very specific to your dataset Input to prediction
4. Generic task Custom task Stuff someone has solved before Very specific to your dataset “puppy” “Sylvester” Input to prediction
5. Generic task Custom task Stuff someone has solved before Very specific to your dataset Pre-trained models (Machine Learning APIs) Cloud Machine Learning Engine Application developers Data scientists and ML practitioners
6. Generic task Custom task Stuff someone has solved before Very specific to your dataset Pre-trained models (Machine Learning APIs) Cloud Machine Learning Engine Application developers Data scientists and ML practitioners Cloud AutoML Lets you train your own custom machine learning models without writing model code
7. • Pre-trained models can accomplish the following ML tasks Cloud Translation Cloud Video Intelligence Cloud Speech to Text & Text to Speech Cloud Vision Cloud Natural Language
8. Powerful image analysis Google Cloud Vision API allows you to explore powerful image analysis with • Pre-trained models • Ability to build custom models using AutoML Vision Easy-to-use REST API Cloud Vision
9. Sometime in late 2017, I published Toodoo, a Firebase-powered app that basically lets you add tasks you want to be reminded of, but with some Machine Learning capabilities.
10. Toodoo is not special. Except that it solves the problem of: Users spending large amount of time and/or having trouble in trying to read small printed material in a voluminous page
11. Hence, allowing users to: • Hunt for text material (in languages such as Chinese, English, Finnish, French, German, Japanese, Korean, Portuguese & Spanish) with a device camera to capture an image. • Process the image and extract the text from it using powerful Google Cloud vision Optical Character Recognition (OCR) support. • Copy the extracted image to clipboard, and add them as to-dos
12. Easily detect broad sets of objects in your images With AutoML Vision, you can create custom models that highlight specific concepts from your images. This enables use cases ranging from categorizing product images to diagnosing diseases. Cloud Vision API also lets you
13. Extracts and identifies text Optical Character Recognition (OCR) enables you to detect text within your images or via the camera, along with automatic language identification. Remember Toodoo?
14. Power of the web Vision API uses the power of Google Image to find topical entities like celebrities, logos, or news events. Millions of entities are supported, so you can be confident that the latest relevant images are available.
15. Moderates content With the power of Google SafeSearch, one can easily moderate content and detect inappropriate content from crowd-sourced images. That is, detect different types of inappropriate content, from adult to violent content.
16. Let’s check it out! cloud.google.com/vision
20. Google Cloud pre-trained ML APIs allows you to do all that machine learning, but there’s more… What if there’s need to train these APIs on your own custom data?
21. • Cloud AutoML allows you to train custom machine learning models for vision, natural language, and translation without writing model code Cloud Translation Cloud Vision Cloud Natural Language
22. Identify your own puppy among hundreds of puppies “Sylvester” “Driver” here could be referred to as ‘taxi driver’, for example Importance of specific prediction or translation
23. How Cloud AutoML works
24. Let’s see how it works! cloud.google.com/automl/ui/vision
25. Click ‘Get started with AutoML’ Note that you need to enable billing
26. So, I love Wilson’s Lemonade, and tried out a very small amount of photos of it’s three different types.
27. Note that each label should have at least 100 images for best results. However, for this sample, I used about 10 each (do not try this at home).
28. Sorted them into their different ‘Labels’.
29. Here’s the evaluation metrics after training
30. Had a number of ‘False negatives’ caused by a few confusing images :(
31. Tested the custom model anyway :D Here’s a wrong prediction for two apparent reasons: 1. Not enough data 2. The Tea label had a number of false negatives We can, of course, remove the confusing images, add more data, and train again. The Pink label had a better precision-recall tradeoff curve, hence a new image was predicted right.
32. You can now work with your custom model
33. AutoML is great, yeah! However, what if you have a custom task that requires more control over your model type and its inputs?
34. • Cloud ML Engine allows you to build, train, and serve custom models with your own data
35. Start here: console.cloud.google.com/mlengine
36. • Use pre-trained APIs to accomplish common ML tasks like image analysis, natural language processing, or translation • Use AutoML to train a model on your own data without writing model code • Use ML Engine to build a model with your data for custom tasks Three things to remember
37. Already got great ideas?
38. cloud.google.com/docs/