This document discusses the Graphlab Create Python package for machine learning. It provides tools for data frames, graph analysis, common algorithms like PageRank and clustering, as well as linear regression, boosted decision trees, classification models, topic modeling, and recommendation systems. Graphlab Create also offers tools for deployment and interactive GUIs.

2.  python package
 SFrame: data frame/tabular data, capable of
storing data to disk and stream it during
computations
 SGraph for graph analysis
 neat integration with iPython
 Graphlab Canvas for interactive GUIs

3.  Can infer nested data. eg. dict is a valid
feature type, GraphLab will explode it to form
features on it’s own

4. Standard algorithms already implemented
connected component
graph coloring
K-Core decomposition
PageRank
Single-source shortest path
Triangle count

5.  Linear regression
feature scaling, missing value imputation
 Boosted Decision Trees
hyperparameter (max_iterations, max_depth,
step_size, min_child_weight, min_loss_reduction ..)
 wrapper ‘regression.create' method which selects
best regression model based on ‘some’ parameter
 wrapper over Vowpal Wabbit is present
 Regularisation support is there (lasso, ridge, enet)
 No randomForest !

6.  Logistic regression
 SVM
 Boosted Decision Trees
 Neural network classifier (deep learning)
selects a default network architecture (2-layer
Perceptron Network for dense numeric input, 1-layer
Convolution Network for image)
custom architecture possible
pre-built models trained on imageNet present, can be
used directly to extract features
 model selector present 'classifier.create'

7.  Bag of words
 TF-IDF
 Topic model

8. Out of box ‘recommender.create’ available

9. Single click push to deployment to multiple
environments (EC2, etc)

10. Single click push to deployment to multiple
environments (EC2, etc)