A developer's overview of the world of predictive APIs
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Predictive APIs are making it easier to integrate Machine Learning in your apps and to add predictive features to them. Starting with some basics we'll see what the different types of APIs are and we'll give some examples of proprietary predictive APIs. We'll go over some ways of exposing your own predictive models as APIs served by 3rd party platforms, and open source frameworks for creating and serving your own APIs on your infrastructure of choice. We'll give some remarks on recent (and missing) tools to make it easier to use and compare all these APIs. Finally, we'll give some pointers to a Virtual Machine to help you get started with these technologies... Slides from my talk at the Valencian Summer School on Machine Learning (#VSMML15)
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A developer's overview of the world of predictive APIs
- 1. A developers’ overview of the world of predictive APIs (proprietary, open source, hybrid) Louis Dorard PAPIs.io
- 2. Predictive APIs basics Types of Predictive APIs Deploy own model on 3rd party platform Open source PAPI frameworks What’s missing / almost there
- 5. The two phases of machine learning: • TRAIN a model • PREDICT with a model
- 6. The two methods of predictive APIs: • TRAIN a model • PREDICT with a model
- 7. The two methods of predictive APIs: • model = create_model(dataset) • predicted_output = create_prediction(model, new_input)
- 8. The two methods of predictive APIs: • model = create_model(‘training.csv’) • predicted_output = create_prediction(model, new_input)
- 9. from bigml.api import BigML # create a model api = BigML() source = api.create_source('training_data.csv') dataset = api.create_dataset(source) model = api.create_model(dataset) # make a prediction prediction = api.create_prediction(model, new_input) print "Predicted output value: ",prediction['object'] ['output'] BigML Python
- 10. # from http://scikit-learn.org/stable/tutorial/ basic/tutorial.html from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) Scikit Python
- 11. $ curl https://bigml.io/model?$BIGML_AUTH -X POST -H "content-type: application/json" -d '{"dataset": "dataset/50ca447b3b56356ae0000029"}' BigML REST http
- 12. The two methods of predictive APIs: • model = create_model(‘training.csv’) • predicted_output = create_prediction(model, new_input)
- 13. The two methods of predictive APIs: • model = create_model(‘training.csv’) • predicted_output = create_prediction(model, new_input)
- 14. Types of Predictive APIs
- 17. “Is this email important? — Yes/No”
- 18. Generic Predictive APIs: • Google Prediction API • BigML • Datagami • PredicSis • WolframCloud • Amazon
- 19. AMAZON GOOGLE PREDICSIS BIGML ACCURACY 0.862 0.743 0.858 0.790 TRAINING TIME 135s 76s 17s 5s TEST TIME 188s 369s 5s 1s louisdorard.com/blog/machine-learning-apis-comparison
- 21. “Which project does this document relate to? — A/B/C”
- 22. Text classification APIs: • uClassify.com • MonkeyLearn.com • Cortical.io
- 24. “Is this customer going to leave next month? — Yes/No”
- 25. “Is this transaction fraudulent? — Yes/No”
- 26. Problem-specific Predictive APIs: • Churn: ChurnSpotter.io, Framed.io • Fraud detection: SiftScience.com • Lead scoring: Infer.com • Personal assistant (Siri): Wit.ai
- 28. “What is the sentiment of this tweet? — Positive/Neutral/Negative”
- 29. “Is this email spam? — Yes/No”
- 30. Fixed-model Predictive APIs: • Text: Datumbox.com, Semantria.com • Vision: Indico, AlchemyAPI.com • Siri-like: maluuba.com
- 33. Deploy your own model on 3rd party platform
- 34. EXPERIMENT / DEPLOY Find model Use model
- 35. Start with {popular, open source} ML library
- 36. Experiment in automated environment
- 42. Experiment on “ScienceCluster” • Distributed jobs • Collaborative workspace • Serialize chosen model Deploy model as API on “ScienceOps” • Load balancing • Auto scaling • Monitoring (API calls, accuracy)
- 43. 43 • 1 for serving predictions • 1 for running ML experiment (i.e. train and evaluate models on given data)? • 1 for deploying ML models? Your API endpoints
- 46. • “Open source prediction server” • Based on Spark • Expose predictive models as (scalable & robust) APIs
- 47. MLlib
- 49. = PAPI+
- 50. • Train command: “pio train” • API endpoints: • Send (new) data • Send prediction queries
- 52. Deployment • Amazon CloudFormation template → cluster • Manual up/down scaling
- 56. What’s missing / almost there
- 57. 57 • Spearmint: “Bayesian optimization” for tuning parameters → Whetlab → Twitter • Auto-sklearn: “automated machine learning toolkit and drop-in replacement for a scikit-learn estimator” • See automl.org and challenge Open Source AutoML?!
- 58. from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) Scikit Python
- 59. from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) Scikit Python
- 60. import autosklearn model = autosklearn.AutoSklearnClassifier() from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) AutoML Scikit
- 61. 61 • ? Open Source Auto Scaling?!
- 62. AMAZON GOOGLE PREDICSIS BIGML ACCURACY 0.862 0.743 0.858 0.790 TRAINING TIME 135s 76s 17s 5s TEST TIME 188s 369s 5s 1s louisdorard.com/blog/machine-learning-apis-comparison
- 63. 63 • Requirement: • train/test splits on local machine • compute evaluation on local machine • Solutions • adapt bigmler and use local evaluations? • use scikit-learn framework? Automated Benchmark?!
- 64. 64 • Python defacto standard: scikit-learn • “Sparkit-learn aims to provide scikit- learn functionality and API on PySpark. The main goal of the library is to create an API that stays close to sklearn’s." • REST standard: PSI (Protocols & Structures for Inference) • Pretty similar to BigML API! • Implementation for scikit available • Easy benchmarking! Ensembles! API standards?!
- 65. 65 • VM with Jupyter notebooks (Python & Bash) • API wrappers preinstalled: BigML & Google Pred • Notebook for easy setup of credentials • Scikit-learn and Pandas preinstalled • Open source VM provisioning script & notebooks • Search public Snaps on terminal.com: “machine learning” Getting started