Introduction to Google Cloud platform technologies


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This is a presentation given by Google Developer Advocate Chris Schalk at Spring One 2GX on Oct 21st, 2010. It introduces Google Storage for Developers, Prediction API, and BigQuery.

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Introduction to Google Cloud platform technologies

  1. 1. Chicago, October 19 - 22, 2010 How to analyze your data and take advantage of machine learning in your application Chris Schalk - Google
  2. 2. SpringOne 2GX 2009. All rights reserved. Do not distribute without Google’s new Cloud Technologies Topics covered •  Google Storage for Developers •  Prediction API (machine learning) •  BigQuery
  3. 3. Google Storage for Developers Store your data in Google's cloud
  4. 4. What Is Google Storage? •  Store your data in Google's cloud o  any format, any amount, any time •  You control access to your data o  private, shared, or public •  Access via Google APIs or 3rd party tools/libraries
  5. 5. Sample Use Cases Static content hosting e.g. static html, images, music, video Backup and recovery e.g. personal data, business records Sharing e.g. share data with your customers Data storage for applications e.g. used as storage backend for Android, AppEngine, Cloud based apps Storage for Computation e.g. BigQuery, Prediction API
  6. 6. Google Storage Benefits High Performance and Scalability Backed by Google infrastructure Strong Security and Privacy Control access to your data Easy to Use Get started fast with Google & 3rd party tools
  7. 7. Google Storage Technical Details •  RESTful API  o  Verbs: GET, PUT, POST, HEAD, DELETE  o  Resources: identified by URI o  Compatible with S3  •  Buckets  o  Flat containers  •  Objects  o  Any type o  Size: 100 GB / object •  Access Control for Google Accounts  o  For individuals and groups •  Two Ways to Authenticate Requests  o  Sign request using access keys  o  Web browser login
  8. 8. Performance and Scalability •  Objects of any type and 100 GB / Object •  Unlimited numbers of objects, 1000s of buckets •  All data replicated to multiple US data centers •  Leveraging Google's worldwide network for data delivery •  Only you can use bucket names with your domain names •  Read-your-writes data consistency •  Range Get
  9. 9. Security and Privacy Features •  Key-based authentication •  Authenticated downloads from a web browser •  Sharing with individuals •  Group sharing via Google Groups •  Access control for buckets and objects •  Set Read/Write/List permissions
  10. 10. Demo
  11. 11. Demo •  Tools: o  GS Manager o  GSUtil •  Upload / Download
  12. 12. Google Storage usage within Google Haiti Relief Imagery USPTO data Partner Reporting Google BigQuery Google Prediction API Partner Reporting
  13. 13. Some Early Google Storage Adopters
  14. 14. Google Storage - Pricing o  Storage  $0.17/GB/Month o  Network  Upload - $0.10/GB  Download  $0.15/GB Americas / EMEA  $0.30/GB  APAC o  Requests  PUT, POST, LIST - $0.01 / 1000 Requests  GET, HEAD - $0.01 / 10000 Requests
  15. 15. Google Storage - Availability •  Limited preview in US currently o  100GB free storage and network from Google per account o  Sign up for waitlist at storage/ •  Note: Non US preview available on case-by-case basis
  16. 16. Google Storage Summary •  Store any kind of data using Google's cloud infrastructure •  Easy to Use APIs •  Many available tools and libraries o  gsutil, GS Manager o  3rd party:  Boto, CloudBerry, CyberDuck, JetS3t, and more
  17. 17. Google Prediction API Google's prediction engine in the cloud
  18. 18. Introducing the Google Prediction API •  Google's sophisticated machine learning technology •  Available as an on-demand RESTful HTTP web service
  19. 19. How does it work? "english" The quick brown fox jumped over the lazy dog. "english" To err is human, but to really foul things up you need a computer. "spanish" No hay mal que por bien no venga. "spanish" La tercera es la vencida. ? To be or not to be, that is the question. ? La fe mueve montañas. The Prediction API finds relevant features in the sample data during training. The Prediction API later searches for those features during prediction.
  20. 20. A virtually endless number of applications... Customer Sentiment Transaction Risk Species Identification Message Routing Legal Docket Classification Suspicious Activity Work Roster Assignment Recommend Products Political Bias Uplift Marketing Email Filtering Diagnostics Inappropriate Content Career Counselling Churn Prediction ... and many more ...
  21. 21. A Prediction API Example Automatically categorize and respond to emails by language •  Customer: ACME Corp, a multinational organization •  Goal: Respond to customer emails in their language •  Data: Many emails, tagged with their languages •  Outcome: Predict language and respond accordingly
  22. 22. Using the Prediction API 1. Upload 2. Train Upload your training data to Google Storage Build a model from your data Make new predictions3. Predict A simple three step process...
  23. 23. Step 1: Upload Upload your training data to Google Storage •  Training data: outputs and input features •  Data format: comma separated value format (CSV) "english","To err is human, but to really ..." "spanish","No hay mal que por bien no venga." ... Upload to Google Storage gsutil cp ${data} gs://yourbucket/${data}
  24. 24. Step 2: Train Create a new model by training on data To train a model: POST prediction/v1.1/training?data=mybucket%2Fmydata Training runs asynchronously. To see if it has finished: GET prediction/v1.1/training/mybucket%2Fmydata {"data":{ "data":"mybucket/mydata", "modelinfo":"estimated accuracy: 0.xx"}}}
  25. 25. Step 3: Predict Apply the trained model to make predictions on new data POST prediction/v1.1/query/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}}
  26. 26. Step 3: Predict Apply the trained model to make predictions on new data POST prediction/v1.1/query/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}} { data : { "kind" : "prediction#output", "outputLabel":"French", "outputMulti" :[ {"label":"French", "score": x.xx} {"label":"English", "score": x.xx} {"label":"Spanish", "score": x.xx}]}}
  27. 27. Step 3: Predict Apply the trained model to make predictions on new data import httplib header = {"Content-Type" : "application/json"} #...put new data in JSON format in params variable conn = httplib.HTTPConnection("")conn.request("POST", "/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header) print conn.getresponse() An example using Python
  28. 28. Prediction API Capabilities Data •  Input Features: numeric or unstructured text •  Output: up to hundreds of discrete categories Training •  Many machine learning techniques •  Automatically selected •  Performed asynchronously Access from many platforms: •  Web app from Google App Engine •  Apps Script (e.g. from Google Spreadsheet) •  Desktop app
  29. 29. Prediction API v1.1 - new features •  Updated Syntax •  Multi-category prediction o  Tag entry with multiple labels •  Continuous Output o  Finer grained prediction rankings based on multiple labels •  Mixed Inputs o  Both numeric and text inputs are now supported Can combine continuous output with mixed inputs
  30. 30. Demo Using the Prediction API to Predict a Cuisine
  31. 31. Google BigQuery Interactive analysis of large datasets in Google's cloud
  32. 32. Introducing Google BigQuery •  Google's large data adhoc analysis technology o  Analyze massive amounts of data in seconds •  Simple SQL-like query language •  Flexible access o  REST APIs, JSON-RPC, Google Apps Script
  33. 33. Why BigQuery? Working with large data is a challenge
  34. 34. Many Use Cases ... Spam Trends Detection Web Dashboards Network Optimization Interactive Tools
  35. 35. Key Capabilities of BigQuery •  Scalable: Billions of rows •  Fast: Response in seconds •  Simple: Queries in SQL •  Web Service o  REST o  JSON-RPC o  Google App Scripts
  36. 36. Using BigQuery 1. Upload 2. Import Upload your raw data to Google Storage Import raw data into BigQuery table Perform SQL queries on table3. Query Another simple three step process...
  37. 37. Writing Queries Compact subset of SQL o  SELECT ... FROM ... WHERE ... GROUP BY ... ORDER BY ... LIMIT ...; Common functions o  Math, String, Time, ... Statistical approximations o  TOP o  COUNT DISTINCT
  38. 38. BigQuery via REST GET /bigquery/v1/tables/{table name} GET /bigquery/v1/query?q={query} Sample JSON Reply: { "results": { "fields": { [ {"id":"COUNT(*)","type":"uint64"}, ... ] }, "rows": [ {"f":[{"v":"2949"}, ...]}, {"f":[{"v":"5387"}, ...]}, ... ] } } Also supports JSON-RPC
  39. 39. Security and Privacy Standard Google Authentication •  Client Login •  OAuth •  AuthSub HTTPS support •  protects your credentials •  protects your data Relies on Google Storage to manage access
  40. 40. Large Data Analysis Example Wikimedia Revision history data from: enwiki-latest-pages-meta-history.xml.7z Wikimedia Revision History
  41. 41. Using BigQuery Shell Python DB API 2.0 + B. Clapper's sqlcmd
  42. 42. BigQuery from a Spreadsheet
  43. 43. BigQuery from a Spreadsheet
  44. 44. Recap •  Google Storage o  High speed data storage on Google Cloud •  Prediction API o  Google's machine learning technology able to predict outcomes based on sample data •  BigQuery o  Interactive analysis of very large data sets o  Simple SQL query language access
  45. 45. Further info available at: •  Google Storage for Developers o •  Prediction API o •  BigQuery o
  46. 46. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission. Demo
  47. 47. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission. Q&A
  48. 48. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission. Thank You! Chris Schalk Google Developer Advocate